world-bank-data-eda.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis on the World Bank Data\n",
    "\n",
    "The objective of this project is to collect data from the World Bank Open APIs and prepare and analyse the data using Python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import requests\n",
    "\n",
    "from IPython.display import display\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task-1: Data Identification\n",
    "\n",
    "For this analysis, the following API has been selected: https://datahelpdesk.worldbank.org/knowledgebase/articles/889392-api-documentation\n",
    "\n",
    "The World Bank data consists of demographic and other statistical data related to Population, Employment, Health, GDP, Energy Consumption, etc. for all the countries from the year 1960 to 2018. These categories are called indicators and are each defined by a code.\n",
    "\n",
    "The following indicators have been chosen for analysis:\n",
    "* SP.POP.TOTL - Total Population\n",
    "* SP.POP.TOTL.FE.IN - Total Female Population\n",
    "* SP.POP.TOTL.MA.IN - Total Male Population\n",
    "* SP.DYN.CBRT.IN Birth Rate\n",
    "* SP.DYN.CDRT.IN Death Rate\n",
    "* SE.COM.DURS - Compulsory Education Duration\n",
    "* SL.IND.EMPL.ZS - Employment in Industry(%)\n",
    "* SL.AGR.EMPL.ZS - Employment in Agriculture(%)\n",
    "* SL.AGR.EMPL.FE.ZS - Female Employment in Agriculture(%)\n",
    "* SL.IND.EMPL.FE.ZS - Female Employment in Industry(%)\n",
    "* SL.UEM.TOTL.ZS - Unemployment(%)\n",
    "* NY.GDP.MKTP.CD - GDP in USD\n",
    "* NY.ADJ.NNTY.PC.KD.ZG - National Income per Capita\n",
    "* NY.GSR.NFCY.CD - Net income from Abroad\n",
    "* NV.AGR.TOTL.CD - Agriculture value added(in USD)\n",
    "* EG.USE.ELEC.KH.PC - Electric Power Consumption(kWH per capita)\n",
    "* EG.FEC.RNEW.ZS - Renewable Energy Consumption (%)\n",
    "* EG.USE.COMM.FO.ZS - Fossil Fuel Consumption (%)\n",
    "  \n",
    "The following countries have been chosen for analysis:\n",
    "* US - United States of America\n",
    "* IN - India\n",
    "* CN - China\n",
    "* JP - Japan\n",
    "* CA - Canada\n",
    "* GB - Great Britain\n",
    "* ZA - South Africa\n",
    "\n",
    "The base API URL is http://api.worldbank.org/v2/ and is followed by the country code and indicator code to obtain the data for each year.\n",
    "No authentication is required to use the API."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task-2: Data Collection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Defining Constants\n",
    "\n",
    "Since the API uses codes, we have created a mapping between the codes and meaningful names to use while parsing and storing the data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Base URL used in all the API calls\n",
    "BASE_URL='http://api.worldbank.org/v2/'\n",
    "\n",
    "# List of indicators according to the features defined above\n",
    "INDICATOR_CODES=['SP.POP.TOTL', 'SP.POP.TOTL.FE.IN', 'SP.POP.TOTL.MA.IN',\n",
    " 'SP.DYN.CBRT.IN', 'SP.DYN.CDRT.IN',\n",
    " 'SE.COM.DURS',\n",
    " 'SL.IND.EMPL.ZS', 'SL.AGR.EMPL.ZS', 'SL.AGR.EMPL.FE.ZS', 'SL.IND.EMPL.FE.ZS', 'SL.UEM.TOTL.ZS',\n",
    " 'NY.GDP.MKTP.CD',\n",
    " 'NY.ADJ.NNTY.PC.KD.ZG', 'NY.GSR.NFCY.CD', 'NV.AGR.TOTL.CD',\n",
    " 'EG.USE.ELEC.KH.PC', 'EG.FEC.RNEW.ZS', 'EG.USE.COMM.FO.ZS']\n",
    "\n",
    "COUNTRY_LIST=['USA', 'India', 'China', 'Japan', 'Canada', 'Great Britain', 'South Africa']\n",
    "\n",
    "# mapping of feature codes to more meaningful names\n",
    "featureMap={\n",
    "    \"SP.POP.TOTL\": \"Total Population\",\n",
    "    \"SP.POP.TOTL.FE.IN\": \"Female Population\",\n",
    "    \"SP.POP.TOTL.MA.IN\": \"Male Population\",\n",
    "    \"SP.DYN.CBRT.IN\": \"Birth Rate\",\n",
    "    \"SP.DYN.CDRT.IN\": \"Death Rate\",\n",
    "    \"SE.COM.DURS\": \"Compulsory Education Dur.\",\n",
    "    \"SL.IND.EMPL.ZS\":\"Employment in Industry(%)\",\n",
    "    \"SL.AGR.EMPL.ZS\": \"Employment in Agriculture(%)\",\n",
    "    \"SL.AGR.EMPL.FE.ZS\": \"Female Employment in Agriculture(%)\",\n",
    "    \"SL.IND.EMPL.FE.ZS\": \"Female Employment in Industry(%)\",\n",
    "    \"SL.UEM.TOTL.ZS\": \"Unemployment(%)\",\n",
    "    \"NY.GDP.MKTP.CD\": \"GDP in USD\",\n",
    "    \"NY.ADJ.NNTY.PC.KD.ZG\":\"National Income per Capita\",\n",
    "    \"NY.GSR.NFCY.CD\":\"Net income from Abroad\",\n",
    "    \"NV.AGR.TOTL.CD\":\"Agriculture value added(in USD)\",\n",
    "    \"EG.USE.ELEC.KH.PC\":\"Electric Power Consumption(kWH per capita)\",\n",
    "    \"EG.FEC.RNEW.ZS\":\"Renewable Energy Consumption (%)\",\n",
    "    \"EG.USE.COMM.FO.ZS\":\"Fossil Fuel Consumption (%)\"\n",
    "}\n",
    "\n",
    "# Mapping of country codes to their actual names\n",
    "countryMap={\n",
    "    \"US\": \"USA\",\n",
    "    \"IN\":\"India\",\n",
    "    \"CN\": \"China\",\n",
    "    \"JP\": \"Japan\",\n",
    "    \"CA\": \"Canada\",\n",
    "    \"GB\": \"Great Britain\",\n",
    "    \"ZA\": \"South Africa\"\n",
    "}\n",
    "\n",
    "# constant parameters used in sending the request.\n",
    "params = dict()\n",
    "# to ensure we receive a JSON response\n",
    "params['format']='json'\n",
    "# The data we fetch is for 59 years.\n",
    "# Hence we change the default page size of 50 to 100 to ensure we need only one API call per feature.\n",
    "params['per_page']='100'\n",
    "# Range of years for which the data is needed\n",
    "params['date']='1960:2018'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fetch data through API calls\n",
    "    \n",
    "The following functions have been written to make an API call per feature and fetch the data. Each of these functions performs the following tasks:\n",
    "\n",
    "* **getCountrywiseDF():** Calls the loadJSONData function for the country specified and returns a dataframe with the complete data for one country.\n",
    "* **loadJSONData():** Forms the appropriate URL using the base URL, country code and indicator code and sends the request to the endpoint. It returns a list of list of values for all features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to get JSON data from the endpoint\n",
    "def loadJSONData(country_code): \n",
    "    dataList=[]\n",
    "    \n",
    "    # iterate over each indicator code specified in the contant INDICATOR_CODES defined above\n",
    "    for indicator in INDICATOR_CODES: \n",
    "        \n",
    "        # form the URL in the desired format\n",
    "        # E.g: http://api.worldbank.org/v2/countries/us/indicators/SP.POP.TOTL?format=json&per_page=200&date=1960:2018\n",
    "        url=BASE_URL+'countries/'+country_code.lower()+'/indicators/'+indicator\n",
    "        \n",
    "        # send the request using the resquests module\n",
    "        response = requests.get(url, params=params)\n",
    "        \n",
    "        # validate the response status code\n",
    "        # The API returns a status_code 200 even for error messages,\n",
    "        # however, the response body contains a field called \"message\" that includes the details of the error\n",
    "        # check if message is not present in the response\n",
    "        if response.status_code == 200 and (\"message\" not in response.json()[0].keys()):\n",
    "            # print(\"Successfully got data for: \" + str(featureMap[indicator]))\n",
    "            \n",
    "            # list of values for one feature\n",
    "            indicatorVals=[]\n",
    "            \n",
    "            # the response is an array containing two arrays - [[{page: 1, ...}], [{year: 2018, SP.POP.TOTL: 123455}, ...]]\n",
    "            # hence we check if the length of the response is >1\n",
    "            if len(response.json()) > 1:\n",
    "                \n",
    "                # if yes, iterate over each object in the response\n",
    "                # each object gives one single value for each year\n",
    "                for obj in response.json()[1]:\n",
    "                    \n",
    "                    # check for empty values\n",
    "                    if obj['value'] is \"\" or obj['value'] is None:\n",
    "                        indicatorVals.append(None)\n",
    "                    else:\n",
    "                    # if a value is present, add it to the list of indicator values\n",
    "                        indicatorVals.append(float(obj['value']))\n",
    "                dataList.append(indicatorVals)\n",
    "        else:\n",
    "            # print an error message if the API call failed\n",
    "            print(\"Error in Loading the data. Status Code: \" + str(response.status_code))\n",
    "            \n",
    "    # Once all the features have been obtained, add the values for the \"Year\"\n",
    "    # The API returns the indicator values from the most recent year. Hence, we create a list of years in reverse order\n",
    "    dataList.append([year for year in range(2018, 1959, -1)])\n",
    "    # return the list of lists of feature values [[val1,val2,val3...], [val1,val2,val3...], [val1,val2,val3...], ...]\n",
    "    return dataList\n",
    "\n",
    "#----------------------------------------------------------------------------------------------------\n",
    "\n",
    "# function to invokde the loadJSONData function and form the final DataFrame for each country\n",
    "def getCountrywiseDF(country_code):\n",
    "    \n",
    "    # The resulting dataframe needs to have meaningful column names\n",
    "    # hence we create a list of column names from the map defined above\n",
    "    col_list=list(featureMap.values())\n",
    "    # append the year column name\n",
    "    col_list.append('Year')\n",
    "    \n",
    "    print(\"------------------Loading data for: \"+countryMap[country_code]+\"-----------------------\")\n",
    "    \n",
    "    # for the given country call the loadJSONData function and fetch the data from the API\n",
    "    dataList=loadJSONData(country_code)\n",
    "    \n",
    "    # transform the list of lists of features into a DataFrame\n",
    "    # np.column_stack is used to add each list as a column \n",
    "    df=pd.DataFrame(np.column_stack(dataList), columns=col_list)\n",
    "    \n",
    "    # add the country column by extracting the country name from the map using the country code\n",
    "    df['Country'] = countryMap[country_code]\n",
    "    \n",
    "    # display the resulting dataframe\n",
    "    display(df.head())\n",
    "    \n",
    "    # return the formed dataframe for the given country\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------Loading data for: USA-----------------------\n"
     ]
    },
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total Population</th>\n",
       "      <th>Female Population</th>\n",
       "      <th>Male Population</th>\n",
       "      <th>Birth Rate</th>\n",
       "      <th>Death Rate</th>\n",
       "      <th>Compulsory Education Dur.</th>\n",
       "      <th>Employment in Industry(%)</th>\n",
       "      <th>Employment in Agriculture(%)</th>\n",
       "      <th>Female Employment in Agriculture(%)</th>\n",
       "      <th>Female Employment in Industry(%)</th>\n",
       "      <th>Unemployment(%)</th>\n",
       "      <th>GDP in USD</th>\n",
       "      <th>National Income per Capita</th>\n",
       "      <th>Net income from Abroad</th>\n",
       "      <th>Agriculture value added(in USD)</th>\n",
       "      <th>Electric Power Consumption(kWH per capita)</th>\n",
       "      <th>Renewable Energy Consumption (%)</th>\n",
       "      <th>Fossil Fuel Consumption (%)</th>\n",
       "      <th>Year</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>327,167,434.0</td>\n",
       "      <td>165,285,036.0</td>\n",
       "      <td>161,882,398.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>12.0</td>\n",
       "      <td>19.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.8</td>\n",
       "      <td>8.5</td>\n",
       "      <td>3.9</td>\n",
       "      <td>20,544,343,456,936.5</td>\n",
       "      <td>None</td>\n",
       "      <td>267,900,000,000.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2018</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>325,147,121.0</td>\n",
       "      <td>164,275,323.0</td>\n",
       "      <td>160,871,798.0</td>\n",
       "      <td>11.8</td>\n",
       "      <td>8.5</td>\n",
       "      <td>12.0</td>\n",
       "      <td>19.7</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.8</td>\n",
       "      <td>8.7</td>\n",
       "      <td>4.4</td>\n",
       "      <td>19,485,393,853,000.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>243,296,000,000.0</td>\n",
       "      <td>178,579,605,000.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2017</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>323,071,342.0</td>\n",
       "      <td>163,245,729.0</td>\n",
       "      <td>159,825,613.0</td>\n",
       "      <td>12.2</td>\n",
       "      <td>8.5</td>\n",
       "      <td>12.0</td>\n",
       "      <td>19.8</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.8</td>\n",
       "      <td>8.7</td>\n",
       "      <td>4.9</td>\n",
       "      <td>18,707,188,235,000.0</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>218,168,000,000.0</td>\n",
       "      <td>175,923,170,000.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2016</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>320,742,673.0</td>\n",
       "      <td>162,097,641.0</td>\n",
       "      <td>158,645,032.0</td>\n",
       "      <td>12.4</td>\n",
       "      <td>8.4</td>\n",
       "      <td>12.0</td>\n",
       "      <td>19.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.7</td>\n",
       "      <td>8.8</td>\n",
       "      <td>5.3</td>\n",
       "      <td>18,219,297,584,000.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>224,626,000,000.0</td>\n",
       "      <td>189,947,276,000.0</td>\n",
       "      <td>None</td>\n",
       "      <td>8.7</td>\n",
       "      <td>82.4</td>\n",
       "      <td>2015</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>318,386,421.0</td>\n",
       "      <td>160,946,480.0</td>\n",
       "      <td>157,439,941.0</td>\n",
       "      <td>12.5</td>\n",
       "      <td>8.2</td>\n",
       "      <td>12.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.7</td>\n",
       "      <td>8.9</td>\n",
       "      <td>6.2</td>\n",
       "      <td>17,521,746,534,000.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>241,062,000,000.0</td>\n",
       "      <td>208,273,224,000.0</td>\n",
       "      <td>12,994.0</td>\n",
       "      <td>8.8</td>\n",
       "      <td>83.1</td>\n",
       "      <td>2014</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "  Total Population Female Population Male Population Birth Rate Death Rate  \\\n",
       "0    327,167,434.0     165,285,036.0   161,882,398.0       None       None   \n",
       "1    325,147,121.0     164,275,323.0   160,871,798.0       11.8        8.5   \n",
       "2    323,071,342.0     163,245,729.0   159,825,613.0       12.2        8.5   \n",
       "3    320,742,673.0     162,097,641.0   158,645,032.0       12.4        8.4   \n",
       "4    318,386,421.0     160,946,480.0   157,439,941.0       12.5        8.2   \n",
       "\n",
       "  Compulsory Education Dur. Employment in Industry(%)  \\\n",
       "0                      12.0                      19.4   \n",
       "1                      12.0                      19.7   \n",
       "2                      12.0                      19.8   \n",
       "3                      12.0                      19.9   \n",
       "4                      12.0                      20.0   \n",
       "\n",
       "  Employment in Agriculture(%) Female Employment in Agriculture(%)  \\\n",
       "0                          1.4                                 0.8   \n",
       "1                          1.4                                 0.8   \n",
       "2                          1.4                                 0.8   \n",
       "3                          1.4                                 0.7   \n",
       "4                          1.4                                 0.7   \n",
       "\n",
       "  Female Employment in Industry(%) Unemployment(%)           GDP in USD  \\\n",
       "0                              8.5             3.9 20,544,343,456,936.5   \n",
       "1                              8.7             4.4 19,485,393,853,000.0   \n",
       "2                              8.7             4.9 18,707,188,235,000.0   \n",
       "3                              8.8             5.3 18,219,297,584,000.0   \n",
       "4                              8.9             6.2 17,521,746,534,000.0   \n",
       "\n",
       "  National Income per Capita Net income from Abroad  \\\n",
       "0                       None      267,900,000,000.0   \n",
       "1                        2.2      243,296,000,000.0   \n",
       "2                       -0.1      218,168,000,000.0   \n",
       "3                        2.4      224,626,000,000.0   \n",
       "4                        2.9      241,062,000,000.0   \n",
       "\n",
       "  Agriculture value added(in USD) Electric Power Consumption(kWH per capita)  \\\n",
       "0                            None                                       None   \n",
       "1               178,579,605,000.0                                       None   \n",
       "2               175,923,170,000.0                                       None   \n",
       "3               189,947,276,000.0                                       None   \n",
       "4               208,273,224,000.0                                   12,994.0   \n",
       "\n",
       "  Renewable Energy Consumption (%) Fossil Fuel Consumption (%)  Year Country  \n",
       "0                             None                        None  2018     USA  \n",
       "1                             None                        None  2017     USA  \n",
       "2                             None                        None  2016     USA  \n",
       "3                              8.7                        82.4  2015     USA  \n",
       "4                              8.8                        83.1  2014     USA  "
      ]
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     "output_type": "display_data"
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    {
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     "output_type": "stream",
     "text": [
      "------------------Loading data for: India-----------------------\n"
     ]
    },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total Population</th>\n",
       "      <th>Female Population</th>\n",
       "      <th>Male Population</th>\n",
       "      <th>Birth Rate</th>\n",
       "      <th>Death Rate</th>\n",
       "      <th>Compulsory Education Dur.</th>\n",
       "      <th>Employment in Industry(%)</th>\n",
       "      <th>Employment in Agriculture(%)</th>\n",
       "      <th>Female Employment in Agriculture(%)</th>\n",
       "      <th>Female Employment in Industry(%)</th>\n",
       "      <th>Unemployment(%)</th>\n",
       "      <th>GDP in USD</th>\n",
       "      <th>National Income per Capita</th>\n",
       "      <th>Net income from Abroad</th>\n",
       "      <th>Agriculture value added(in USD)</th>\n",
       "      <th>Electric Power Consumption(kWH per capita)</th>\n",
       "      <th>Renewable Energy Consumption (%)</th>\n",
       "      <th>Fossil Fuel Consumption (%)</th>\n",
       "      <th>Year</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1,352,617,328.0</td>\n",
       "      <td>649,574,719.0</td>\n",
       "      <td>703,042,609.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>8.0</td>\n",
       "      <td>24.7</td>\n",
       "      <td>43.9</td>\n",
       "      <td>57.1</td>\n",
       "      <td>18.7</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2,718,732,231,257.6</td>\n",
       "      <td>None</td>\n",
       "      <td>-27,691,371,968.3</td>\n",
       "      <td>396,987,486,510.1</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2018</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1,338,658,835.0</td>\n",
       "      <td>642,787,641.0</td>\n",
       "      <td>695,871,194.0</td>\n",
       "      <td>18.1</td>\n",
       "      <td>7.2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>24.5</td>\n",
       "      <td>44.5</td>\n",
       "      <td>57.6</td>\n",
       "      <td>18.7</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2,652,242,857,923.9</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-28,673,153,563.2</td>\n",
       "      <td>414,265,878,403.3</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2017</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1,324,509,589.0</td>\n",
       "      <td>635,908,885.0</td>\n",
       "      <td>688,600,704.0</td>\n",
       "      <td>18.3</td>\n",
       "      <td>7.2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>24.3</td>\n",
       "      <td>45.1</td>\n",
       "      <td>58.2</td>\n",
       "      <td>18.6</td>\n",
       "      <td>2.7</td>\n",
       "      <td>2,290,432,075,123.8</td>\n",
       "      <td>6.4</td>\n",
       "      <td>-26,300,115,592.2</td>\n",
       "      <td>372,190,852,855.1</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2016</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1,310,152,403.0</td>\n",
       "      <td>628,929,065.0</td>\n",
       "      <td>681,223,338.0</td>\n",
       "      <td>18.6</td>\n",
       "      <td>7.2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>24.3</td>\n",
       "      <td>45.6</td>\n",
       "      <td>58.6</td>\n",
       "      <td>18.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>2,103,587,813,812.8</td>\n",
       "      <td>7.6</td>\n",
       "      <td>-24,405,488,068.3</td>\n",
       "      <td>340,244,981,475.1</td>\n",
       "      <td>None</td>\n",
       "      <td>36.0</td>\n",
       "      <td>None</td>\n",
       "      <td>2015</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1,295,604,184.0</td>\n",
       "      <td>621,854,638.0</td>\n",
       "      <td>673,749,546.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>24.4</td>\n",
       "      <td>46.1</td>\n",
       "      <td>59.1</td>\n",
       "      <td>18.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>2,039,127,446,298.6</td>\n",
       "      <td>5.8</td>\n",
       "      <td>-24,112,069,639.3</td>\n",
       "      <td>342,408,945,451.4</td>\n",
       "      <td>804.5</td>\n",
       "      <td>36.7</td>\n",
       "      <td>73.6</td>\n",
       "      <td>2014</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Total Population Female Population Male Population Birth Rate Death Rate  \\\n",
       "0  1,352,617,328.0     649,574,719.0   703,042,609.0       None       None   \n",
       "1  1,338,658,835.0     642,787,641.0   695,871,194.0       18.1        7.2   \n",
       "2  1,324,509,589.0     635,908,885.0   688,600,704.0       18.3        7.2   \n",
       "3  1,310,152,403.0     628,929,065.0   681,223,338.0       18.6        7.2   \n",
       "4  1,295,604,184.0     621,854,638.0   673,749,546.0       19.0        7.2   \n",
       "\n",
       "  Compulsory Education Dur. Employment in Industry(%)  \\\n",
       "0                       8.0                      24.7   \n",
       "1                       8.0                      24.5   \n",
       "2                       8.0                      24.3   \n",
       "3                       8.0                      24.3   \n",
       "4                       8.0                      24.4   \n",
       "\n",
       "  Employment in Agriculture(%) Female Employment in Agriculture(%)  \\\n",
       "0                         43.9                                57.1   \n",
       "1                         44.5                                57.6   \n",
       "2                         45.1                                58.2   \n",
       "3                         45.6                                58.6   \n",
       "4                         46.1                                59.1   \n",
       "\n",
       "  Female Employment in Industry(%) Unemployment(%)          GDP in USD  \\\n",
       "0                             18.7             2.6 2,718,732,231,257.6   \n",
       "1                             18.7             2.6 2,652,242,857,923.9   \n",
       "2                             18.6             2.7 2,290,432,075,123.8   \n",
       "3                             18.7             2.8 2,103,587,813,812.8   \n",
       "4                             18.7             2.8 2,039,127,446,298.6   \n",
       "\n",
       "  National Income per Capita Net income from Abroad  \\\n",
       "0                       None      -27,691,371,968.3   \n",
       "1                        4.0      -28,673,153,563.2   \n",
       "2                        6.4      -26,300,115,592.2   \n",
       "3                        7.6      -24,405,488,068.3   \n",
       "4                        5.8      -24,112,069,639.3   \n",
       "\n",
       "  Agriculture value added(in USD) Electric Power Consumption(kWH per capita)  \\\n",
       "0               396,987,486,510.1                                       None   \n",
       "1               414,265,878,403.3                                       None   \n",
       "2               372,190,852,855.1                                       None   \n",
       "3               340,244,981,475.1                                       None   \n",
       "4               342,408,945,451.4                                      804.5   \n",
       "\n",
       "  Renewable Energy Consumption (%) Fossil Fuel Consumption (%)  Year Country  \n",
       "0                             None                        None  2018   India  \n",
       "1                             None                        None  2017   India  \n",
       "2                             None                        None  2016   India  \n",
       "3                             36.0                        None  2015   India  \n",
       "4                             36.7                        73.6  2014   India  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------Loading data for: China-----------------------\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total Population</th>\n",
       "      <th>Female Population</th>\n",
       "      <th>Male Population</th>\n",
       "      <th>Birth Rate</th>\n",
       "      <th>Death Rate</th>\n",
       "      <th>Compulsory Education Dur.</th>\n",
       "      <th>Employment in Industry(%)</th>\n",
       "      <th>Employment in Agriculture(%)</th>\n",
       "      <th>Female Employment in Agriculture(%)</th>\n",
       "      <th>Female Employment in Industry(%)</th>\n",
       "      <th>Unemployment(%)</th>\n",
       "      <th>GDP in USD</th>\n",
       "      <th>National Income per Capita</th>\n",
       "      <th>Net income from Abroad</th>\n",
       "      <th>Agriculture value added(in USD)</th>\n",
       "      <th>Electric Power Consumption(kWH per capita)</th>\n",
       "      <th>Renewable Energy Consumption (%)</th>\n",
       "      <th>Fossil Fuel Consumption (%)</th>\n",
       "      <th>Year</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1,392,730,000.0</td>\n",
       "      <td>677,972,245.0</td>\n",
       "      <td>714,757,755.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>9.0</td>\n",
       "      <td>28.6</td>\n",
       "      <td>26.8</td>\n",
       "      <td>24.2</td>\n",
       "      <td>28.3</td>\n",
       "      <td>4.4</td>\n",
       "      <td>13,608,151,864,637.9</td>\n",
       "      <td>None</td>\n",
       "      <td>-51,298,699,628.7</td>\n",
       "      <td>978,452,524,165.8</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2018</td>\n",
       "      <td>China</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1,386,395,000.0</td>\n",
       "      <td>674,707,818.0</td>\n",
       "      <td>711,687,182.0</td>\n",
       "      <td>12.4</td>\n",
       "      <td>7.1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>24.4</td>\n",
       "      <td>28.8</td>\n",
       "      <td>4.4</td>\n",
       "      <td>12,143,491,448,186.1</td>\n",
       "      <td>4.8</td>\n",
       "      <td>-9,688,110,315.4</td>\n",
       "      <td>918,794,756,465.6</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2017</td>\n",
       "      <td>China</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1,378,665,000.0</td>\n",
       "      <td>670,780,903.0</td>\n",
       "      <td>707,884,097.0</td>\n",
       "      <td>12.9</td>\n",
       "      <td>7.1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>29.1</td>\n",
       "      <td>27.7</td>\n",
       "      <td>25.1</td>\n",
       "      <td>29.1</td>\n",
       "      <td>4.5</td>\n",
       "      <td>11,137,945,669,350.6</td>\n",
       "      <td>7.5</td>\n",
       "      <td>-44,951,463,616.5</td>\n",
       "      <td>905,097,449,018.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2016</td>\n",
       "      <td>China</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1,371,220,000.0</td>\n",
       "      <td>667,009,225.0</td>\n",
       "      <td>704,210,775.0</td>\n",
       "      <td>12.1</td>\n",
       "      <td>7.1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>29.2</td>\n",
       "      <td>28.6</td>\n",
       "      <td>26.0</td>\n",
       "      <td>29.3</td>\n",
       "      <td>4.6</td>\n",
       "      <td>11,015,542,352,468.9</td>\n",
       "      <td>7.4</td>\n",
       "      <td>-41,788,839,823.4</td>\n",
       "      <td>927,733,440,385.4</td>\n",
       "      <td>None</td>\n",
       "      <td>12.4</td>\n",
       "      <td>None</td>\n",
       "      <td>2015</td>\n",
       "      <td>China</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1,364,270,000.0</td>\n",
       "      <td>663,493,378.0</td>\n",
       "      <td>700,776,622.0</td>\n",
       "      <td>12.4</td>\n",
       "      <td>7.2</td>\n",
       "      <td>9.0</td>\n",
       "      <td>29.4</td>\n",
       "      <td>29.5</td>\n",
       "      <td>26.8</td>\n",
       "      <td>29.7</td>\n",
       "      <td>4.6</td>\n",
       "      <td>10,438,529,153,237.6</td>\n",
       "      <td>7.2</td>\n",
       "      <td>13,298,824,755.0</td>\n",
       "      <td>905,464,400,820.4</td>\n",
       "      <td>3,927.0</td>\n",
       "      <td>12.2</td>\n",
       "      <td>87.7</td>\n",
       "      <td>2014</td>\n",
       "      <td>China</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Total Population Female Population Male Population Birth Rate Death Rate  \\\n",
       "0  1,392,730,000.0     677,972,245.0   714,757,755.0       None       None   \n",
       "1  1,386,395,000.0     674,707,818.0   711,687,182.0       12.4        7.1   \n",
       "2  1,378,665,000.0     670,780,903.0   707,884,097.0       12.9        7.1   \n",
       "3  1,371,220,000.0     667,009,225.0   704,210,775.0       12.1        7.1   \n",
       "4  1,364,270,000.0     663,493,378.0   700,776,622.0       12.4        7.2   \n",
       "\n",
       "  Compulsory Education Dur. Employment in Industry(%)  \\\n",
       "0                       9.0                      28.6   \n",
       "1                       9.0                      29.0   \n",
       "2                       9.0                      29.1   \n",
       "3                       9.0                      29.2   \n",
       "4                       9.0                      29.4   \n",
       "\n",
       "  Employment in Agriculture(%) Female Employment in Agriculture(%)  \\\n",
       "0                         26.8                                24.2   \n",
       "1                         27.0                                24.4   \n",
       "2                         27.7                                25.1   \n",
       "3                         28.6                                26.0   \n",
       "4                         29.5                                26.8   \n",
       "\n",
       "  Female Employment in Industry(%) Unemployment(%)           GDP in USD  \\\n",
       "0                             28.3             4.4 13,608,151,864,637.9   \n",
       "1                             28.8             4.4 12,143,491,448,186.1   \n",
       "2                             29.1             4.5 11,137,945,669,350.6   \n",
       "3                             29.3             4.6 11,015,542,352,468.9   \n",
       "4                             29.7             4.6 10,438,529,153,237.6   \n",
       "\n",
       "  National Income per Capita Net income from Abroad  \\\n",
       "0                       None      -51,298,699,628.7   \n",
       "1                        4.8       -9,688,110,315.4   \n",
       "2                        7.5      -44,951,463,616.5   \n",
       "3                        7.4      -41,788,839,823.4   \n",
       "4                        7.2       13,298,824,755.0   \n",
       "\n",
       "  Agriculture value added(in USD) Electric Power Consumption(kWH per capita)  \\\n",
       "0               978,452,524,165.8                                       None   \n",
       "1               918,794,756,465.6                                       None   \n",
       "2               905,097,449,018.0                                       None   \n",
       "3               927,733,440,385.4                                       None   \n",
       "4               905,464,400,820.4                                    3,927.0   \n",
       "\n",
       "  Renewable Energy Consumption (%) Fossil Fuel Consumption (%)  Year Country  \n",
       "0                             None                        None  2018   China  \n",
       "1                             None                        None  2017   China  \n",
       "2                             None                        None  2016   China  \n",
       "3                             12.4                        None  2015   China  \n",
       "4                             12.2                        87.7  2014   China  "
      ]
     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------Loading data for: Japan-----------------------\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total Population</th>\n",
       "      <th>Female Population</th>\n",
       "      <th>Male Population</th>\n",
       "      <th>Birth Rate</th>\n",
       "      <th>Death Rate</th>\n",
       "      <th>Compulsory Education Dur.</th>\n",
       "      <th>Employment in Industry(%)</th>\n",
       "      <th>Employment in Agriculture(%)</th>\n",
       "      <th>Female Employment in Agriculture(%)</th>\n",
       "      <th>Female Employment in Industry(%)</th>\n",
       "      <th>Unemployment(%)</th>\n",
       "      <th>GDP in USD</th>\n",
       "      <th>National Income per Capita</th>\n",
       "      <th>Net income from Abroad</th>\n",
       "      <th>Agriculture value added(in USD)</th>\n",
       "      <th>Electric Power Consumption(kWH per capita)</th>\n",
       "      <th>Renewable Energy Consumption (%)</th>\n",
       "      <th>Fossil Fuel Consumption (%)</th>\n",
       "      <th>Year</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>126,529,100.0</td>\n",
       "      <td>64,731,350.0</td>\n",
       "      <td>61,797,750.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>9.0</td>\n",
       "      <td>24.5</td>\n",
       "      <td>3.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>4,971,323,079,771.9</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2018</td>\n",
       "      <td>Japan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>126,785,797.0</td>\n",
       "      <td>64,853,783.0</td>\n",
       "      <td>61,932,014.0</td>\n",
       "      <td>7.6</td>\n",
       "      <td>10.8</td>\n",
       "      <td>9.0</td>\n",
       "      <td>24.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.1</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4,859,950,558,539.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>177,764,874,566.3</td>\n",
       "      <td>57,797,299,055.4</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2017</td>\n",
       "      <td>Japan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>126,994,511.0</td>\n",
       "      <td>64,951,468.0</td>\n",
       "      <td>62,043,043.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>10.5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>25.6</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.5</td>\n",
       "      <td>3.1</td>\n",
       "      <td>4,926,667,087,367.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>165,257,107,699.7</td>\n",
       "      <td>59,665,658,303.1</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2016</td>\n",
       "      <td>Japan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>127,141,000.0</td>\n",
       "      <td>65,016,722.0</td>\n",
       "      <td>62,124,278.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.3</td>\n",
       "      <td>9.0</td>\n",
       "      <td>25.9</td>\n",
       "      <td>3.6</td>\n",
       "      <td>3.2</td>\n",
       "      <td>14.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>4,389,475,622,589.0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>168,614,682,836.8</td>\n",
       "      <td>48,894,606,458.7</td>\n",
       "      <td>None</td>\n",
       "      <td>6.3</td>\n",
       "      <td>93.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>Japan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>127,276,000.0</td>\n",
       "      <td>65,075,452.0</td>\n",
       "      <td>62,200,548.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>14.7</td>\n",
       "      <td>3.6</td>\n",
       "      <td>4,850,413,536,037.8</td>\n",
       "      <td>0.5</td>\n",
       "      <td>174,559,801,997.8</td>\n",
       "      <td>51,233,292,919.4</td>\n",
       "      <td>7,819.7</td>\n",
       "      <td>5.6</td>\n",
       "      <td>94.4</td>\n",
       "      <td>2014</td>\n",
       "      <td>Japan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Total Population Female Population Male Population Birth Rate Death Rate  \\\n",
       "0    126,529,100.0      64,731,350.0    61,797,750.0       None       None   \n",
       "1    126,785,797.0      64,853,783.0    61,932,014.0        7.6       10.8   \n",
       "2    126,994,511.0      64,951,468.0    62,043,043.0        7.8       10.5   \n",
       "3    127,141,000.0      65,016,722.0    62,124,278.0        8.0       10.3   \n",
       "4    127,276,000.0      65,075,452.0    62,200,548.0        8.0       10.1   \n",
       "\n",
       "  Compulsory Education Dur. Employment in Industry(%)  \\\n",
       "0                       9.0                      24.5   \n",
       "1                       9.0                      24.6   \n",
       "2                       9.0                      25.6   \n",
       "3                       9.0                      25.9   \n",
       "4                       9.0                      26.0   \n",
       "\n",
       "  Employment in Agriculture(%) Female Employment in Agriculture(%)  \\\n",
       "0                          3.4                                 3.0   \n",
       "1                          3.4                                 3.0   \n",
       "2                          3.5                                 3.0   \n",
       "3                          3.6                                 3.2   \n",
       "4                          3.7                                 3.3   \n",
       "\n",
       "  Female Employment in Industry(%) Unemployment(%)          GDP in USD  \\\n",
       "0                             14.0             2.4 4,971,323,079,771.9   \n",
       "1                             14.1             2.8 4,859,950,558,539.0   \n",
       "2                             14.5             3.1 4,926,667,087,367.5   \n",
       "3                             14.8             3.4 4,389,475,622,589.0   \n",
       "4                             14.7             3.6 4,850,413,536,037.8   \n",
       "\n",
       "  National Income per Capita Net income from Abroad  \\\n",
       "0                       None                   None   \n",
       "1                        1.4      177,764,874,566.3   \n",
       "2                        1.5      165,257,107,699.7   \n",
       "3                        4.1      168,614,682,836.8   \n",
       "4                        0.5      174,559,801,997.8   \n",
       "\n",
       "  Agriculture value added(in USD) Electric Power Consumption(kWH per capita)  \\\n",
       "0                            None                                       None   \n",
       "1                57,797,299,055.4                                       None   \n",
       "2                59,665,658,303.1                                       None   \n",
       "3                48,894,606,458.7                                       None   \n",
       "4                51,233,292,919.4                                    7,819.7   \n",
       "\n",
       "  Renewable Energy Consumption (%) Fossil Fuel Consumption (%)  Year Country  \n",
       "0                             None                        None  2018   Japan  \n",
       "1                             None                        None  2017   Japan  \n",
       "2                             None                        None  2016   Japan  \n",
       "3                              6.3                        93.0  2015   Japan  \n",
       "4                              5.6                        94.4  2014   Japan  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------Loading data for: Canada-----------------------\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total Population</th>\n",
       "      <th>Female Population</th>\n",
       "      <th>Male Population</th>\n",
       "      <th>Birth Rate</th>\n",
       "      <th>Death Rate</th>\n",
       "      <th>Compulsory Education Dur.</th>\n",
       "      <th>Employment in Industry(%)</th>\n",
       "      <th>Employment in Agriculture(%)</th>\n",
       "      <th>Female Employment in Agriculture(%)</th>\n",
       "      <th>Female Employment in Industry(%)</th>\n",
       "      <th>Unemployment(%)</th>\n",
       "      <th>GDP in USD</th>\n",
       "      <th>National Income per Capita</th>\n",
       "      <th>Net income from Abroad</th>\n",
       "      <th>Agriculture value added(in USD)</th>\n",
       "      <th>Electric Power Consumption(kWH per capita)</th>\n",
       "      <th>Renewable Energy Consumption (%)</th>\n",
       "      <th>Fossil Fuel Consumption (%)</th>\n",
       "      <th>Year</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>37,058,856.0</td>\n",
       "      <td>18,674,525.0</td>\n",
       "      <td>18,384,331.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>10.0</td>\n",
       "      <td>19.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.9</td>\n",
       "      <td>8.4</td>\n",
       "      <td>5.9</td>\n",
       "      <td>1,713,341,704,877.0</td>\n",
       "      <td>None</td>\n",
       "      <td>-22,155,520,038.5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2018</td>\n",
       "      <td>Canada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>36,540,268.0</td>\n",
       "      <td>18,417,231.0</td>\n",
       "      <td>18,123,037.0</td>\n",
       "      <td>10.3</td>\n",
       "      <td>7.5</td>\n",
       "      <td>10.0</td>\n",
       "      <td>19.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>6.3</td>\n",
       "      <td>1,646,867,220,617.5</td>\n",
       "      <td>3.9</td>\n",
       "      <td>-19,163,504,935.8</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2017</td>\n",
       "      <td>Canada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>36,109,487.0</td>\n",
       "      <td>18,202,997.0</td>\n",
       "      <td>17,906,490.0</td>\n",
       "      <td>10.6</td>\n",
       "      <td>7.4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>19.6</td>\n",
       "      <td>1.9</td>\n",
       "      <td>1.1</td>\n",
       "      <td>8.5</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1,526,705,529,135.3</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>-18,668,313,909.5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2016</td>\n",
       "      <td>Canada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>35,702,908.0</td>\n",
       "      <td>17,999,309.0</td>\n",
       "      <td>17,703,599.0</td>\n",
       "      <td>10.7</td>\n",
       "      <td>7.4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>19.9</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>6.9</td>\n",
       "      <td>1,552,899,925,233.7</td>\n",
       "      <td>-2.9</td>\n",
       "      <td>-23,844,486,211.7</td>\n",
       "      <td>26,575,200,689.3</td>\n",
       "      <td>None</td>\n",
       "      <td>22.0</td>\n",
       "      <td>74.1</td>\n",
       "      <td>2015</td>\n",
       "      <td>Canada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>35,437,435.0</td>\n",
       "      <td>17,864,828.0</td>\n",
       "      <td>17,572,607.0</td>\n",
       "      <td>10.8</td>\n",
       "      <td>7.3</td>\n",
       "      <td>10.0</td>\n",
       "      <td>20.2</td>\n",
       "      <td>1.7</td>\n",
       "      <td>1.1</td>\n",
       "      <td>8.6</td>\n",
       "      <td>6.9</td>\n",
       "      <td>1,801,480,123,070.8</td>\n",
       "      <td>1.4</td>\n",
       "      <td>-29,178,622,922.1</td>\n",
       "      <td>25,667,412,178.7</td>\n",
       "      <td>15,588.5</td>\n",
       "      <td>22.0</td>\n",
       "      <td>73.2</td>\n",
       "      <td>2014</td>\n",
       "      <td>Canada</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Total Population Female Population Male Population Birth Rate Death Rate  \\\n",
       "0     37,058,856.0      18,674,525.0    18,384,331.0       None       None   \n",
       "1     36,540,268.0      18,417,231.0    18,123,037.0       10.3        7.5   \n",
       "2     36,109,487.0      18,202,997.0    17,906,490.0       10.6        7.4   \n",
       "3     35,702,908.0      17,999,309.0    17,703,599.0       10.7        7.4   \n",
       "4     35,437,435.0      17,864,828.0    17,572,607.0       10.8        7.3   \n",
       "\n",
       "  Compulsory Education Dur. Employment in Industry(%)  \\\n",
       "0                      10.0                      19.5   \n",
       "1                      10.0                      19.5   \n",
       "2                      10.0                      19.6   \n",
       "3                      10.0                      19.9   \n",
       "4                      10.0                      20.2   \n",
       "\n",
       "  Employment in Agriculture(%) Female Employment in Agriculture(%)  \\\n",
       "0                          1.5                                 0.9   \n",
       "1                          1.5                                 1.0   \n",
       "2                          1.9                                 1.1   \n",
       "3                          1.6                                 1.0   \n",
       "4                          1.7                                 1.1   \n",
       "\n",
       "  Female Employment in Industry(%) Unemployment(%)          GDP in USD  \\\n",
       "0                              8.4             5.9 1,713,341,704,877.0   \n",
       "1                              8.5             6.3 1,646,867,220,617.5   \n",
       "2                              8.5             7.0 1,526,705,529,135.3   \n",
       "3                              8.5             6.9 1,552,899,925,233.7   \n",
       "4                              8.6             6.9 1,801,480,123,070.8   \n",
       "\n",
       "  National Income per Capita Net income from Abroad  \\\n",
       "0                       None      -22,155,520,038.5   \n",
       "1                        3.9      -19,163,504,935.8   \n",
       "2                       -0.3      -18,668,313,909.5   \n",
       "3                       -2.9      -23,844,486,211.7   \n",
       "4                        1.4      -29,178,622,922.1   \n",
       "\n",
       "  Agriculture value added(in USD) Electric Power Consumption(kWH per capita)  \\\n",
       "0                            None                                       None   \n",
       "1                            None                                       None   \n",
       "2                            None                                       None   \n",
       "3                26,575,200,689.3                                       None   \n",
       "4                25,667,412,178.7                                   15,588.5   \n",
       "\n",
       "  Renewable Energy Consumption (%) Fossil Fuel Consumption (%)  Year Country  \n",
       "0                             None                        None  2018  Canada  \n",
       "1                             None                        None  2017  Canada  \n",
       "2                             None                        None  2016  Canada  \n",
       "3                             22.0                        74.1  2015  Canada  \n",
       "4                             22.0                        73.2  2014  Canada  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------Loading data for: Great Britain-----------------------\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total Population</th>\n",
       "      <th>Female Population</th>\n",
       "      <th>Male Population</th>\n",
       "      <th>Birth Rate</th>\n",
       "      <th>Death Rate</th>\n",
       "      <th>Compulsory Education Dur.</th>\n",
       "      <th>Employment in Industry(%)</th>\n",
       "      <th>Employment in Agriculture(%)</th>\n",
       "      <th>Female Employment in Agriculture(%)</th>\n",
       "      <th>Female Employment in Industry(%)</th>\n",
       "      <th>Unemployment(%)</th>\n",
       "      <th>GDP in USD</th>\n",
       "      <th>National Income per Capita</th>\n",
       "      <th>Net income from Abroad</th>\n",
       "      <th>Agriculture value added(in USD)</th>\n",
       "      <th>Electric Power Consumption(kWH per capita)</th>\n",
       "      <th>Renewable Energy Consumption (%)</th>\n",
       "      <th>Fossil Fuel Consumption (%)</th>\n",
       "      <th>Year</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>66,488,991.0</td>\n",
       "      <td>33,666,883.0</td>\n",
       "      <td>32,822,108.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>11.0</td>\n",
       "      <td>18.1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>7.6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2,855,296,731,522.0</td>\n",
       "      <td>None</td>\n",
       "      <td>-38,490,804,137.5</td>\n",
       "      <td>18,020,718,920.3</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2018</td>\n",
       "      <td>Great Britain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>66,058,859.0</td>\n",
       "      <td>33,464,674.0</td>\n",
       "      <td>32,594,185.0</td>\n",
       "      <td>11.4</td>\n",
       "      <td>9.2</td>\n",
       "      <td>11.0</td>\n",
       "      <td>18.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.6</td>\n",
       "      <td>7.7</td>\n",
       "      <td>4.3</td>\n",
       "      <td>2,666,229,179,958.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>-31,787,538,497.2</td>\n",
       "      <td>17,431,087,145.4</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2017</td>\n",
       "      <td>Great Britain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>65,595,565.0</td>\n",
       "      <td>33,246,523.0</td>\n",
       "      <td>32,349,042.0</td>\n",
       "      <td>11.8</td>\n",
       "      <td>9.1</td>\n",
       "      <td>11.0</td>\n",
       "      <td>18.5</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>7.6</td>\n",
       "      <td>4.8</td>\n",
       "      <td>2,694,283,209,613.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-64,716,135,083.3</td>\n",
       "      <td>16,288,737,010.4</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2016</td>\n",
       "      <td>Great Britain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>65,128,861.0</td>\n",
       "      <td>33,027,804.0</td>\n",
       "      <td>32,101,057.0</td>\n",
       "      <td>11.9</td>\n",
       "      <td>9.2</td>\n",
       "      <td>11.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>7.7</td>\n",
       "      <td>5.3</td>\n",
       "      <td>2,928,591,002,002.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-66,997,636,392.8</td>\n",
       "      <td>18,200,415,988.6</td>\n",
       "      <td>None</td>\n",
       "      <td>8.7</td>\n",
       "      <td>80.4</td>\n",
       "      <td>2015</td>\n",
       "      <td>Great Britain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>64,613,160.0</td>\n",
       "      <td>32,785,655.0</td>\n",
       "      <td>31,827,505.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>8.8</td>\n",
       "      <td>11.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.7</td>\n",
       "      <td>7.9</td>\n",
       "      <td>6.1</td>\n",
       "      <td>3,063,803,240,208.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>-62,560,715,026.0</td>\n",
       "      <td>20,435,074,169.1</td>\n",
       "      <td>5,129.5</td>\n",
       "      <td>7.4</td>\n",
       "      <td>82.7</td>\n",
       "      <td>2014</td>\n",
       "      <td>Great Britain</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Total Population Female Population Male Population Birth Rate Death Rate  \\\n",
       "0     66,488,991.0      33,666,883.0    32,822,108.0       None       None   \n",
       "1     66,058,859.0      33,464,674.0    32,594,185.0       11.4        9.2   \n",
       "2     65,595,565.0      33,246,523.0    32,349,042.0       11.8        9.1   \n",
       "3     65,128,861.0      33,027,804.0    32,101,057.0       11.9        9.2   \n",
       "4     64,613,160.0      32,785,655.0    31,827,505.0       12.0        8.8   \n",
       "\n",
       "  Compulsory Education Dur. Employment in Industry(%)  \\\n",
       "0                      11.0                      18.1   \n",
       "1                      11.0                      18.2   \n",
       "2                      11.0                      18.5   \n",
       "3                      11.0                      18.7   \n",
       "4                      11.0                      19.0   \n",
       "\n",
       "  Employment in Agriculture(%) Female Employment in Agriculture(%)  \\\n",
       "0                          1.1                                 0.6   \n",
       "1                          1.2                                 0.6   \n",
       "2                          1.1                                 0.6   \n",
       "3                          1.1                                 0.6   \n",
       "4                          1.3                                 0.7   \n",
       "\n",
       "  Female Employment in Industry(%) Unemployment(%)          GDP in USD  \\\n",
       "0                              7.6             4.0 2,855,296,731,522.0   \n",
       "1                              7.7             4.3 2,666,229,179,958.0   \n",
       "2                              7.6             4.8 2,694,283,209,613.3   \n",
       "3                              7.7             5.3 2,928,591,002,002.5   \n",
       "4                              7.9             6.1 3,063,803,240,208.0   \n",
       "\n",
       "  National Income per Capita Net income from Abroad  \\\n",
       "0                       None      -38,490,804,137.5   \n",
       "1                        1.9      -31,787,538,497.2   \n",
       "2                        1.0      -64,716,135,083.3   \n",
       "3                        2.0      -66,997,636,392.8   \n",
       "4                        2.7      -62,560,715,026.0   \n",
       "\n",
       "  Agriculture value added(in USD) Electric Power Consumption(kWH per capita)  \\\n",
       "0                18,020,718,920.3                                       None   \n",
       "1                17,431,087,145.4                                       None   \n",
       "2                16,288,737,010.4                                       None   \n",
       "3                18,200,415,988.6                                       None   \n",
       "4                20,435,074,169.1                                    5,129.5   \n",
       "\n",
       "  Renewable Energy Consumption (%) Fossil Fuel Consumption (%)  Year  \\\n",
       "0                             None                        None  2018   \n",
       "1                             None                        None  2017   \n",
       "2                             None                        None  2016   \n",
       "3                              8.7                        80.4  2015   \n",
       "4                              7.4                        82.7  2014   \n",
       "\n",
       "         Country  \n",
       "0  Great Britain  \n",
       "1  Great Britain  \n",
       "2  Great Britain  \n",
       "3  Great Britain  \n",
       "4  Great Britain  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------Loading data for: South Africa-----------------------\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total Population</th>\n",
       "      <th>Female Population</th>\n",
       "      <th>Male Population</th>\n",
       "      <th>Birth Rate</th>\n",
       "      <th>Death Rate</th>\n",
       "      <th>Compulsory Education Dur.</th>\n",
       "      <th>Employment in Industry(%)</th>\n",
       "      <th>Employment in Agriculture(%)</th>\n",
       "      <th>Female Employment in Agriculture(%)</th>\n",
       "      <th>Female Employment in Industry(%)</th>\n",
       "      <th>Unemployment(%)</th>\n",
       "      <th>GDP in USD</th>\n",
       "      <th>National Income per Capita</th>\n",
       "      <th>Net income from Abroad</th>\n",
       "      <th>Agriculture value added(in USD)</th>\n",
       "      <th>Electric Power Consumption(kWH per capita)</th>\n",
       "      <th>Renewable Energy Consumption (%)</th>\n",
       "      <th>Fossil Fuel Consumption (%)</th>\n",
       "      <th>Year</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>57,779,622.0</td>\n",
       "      <td>29,290,888.0</td>\n",
       "      <td>28,488,734.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>9.0</td>\n",
       "      <td>23.2</td>\n",
       "      <td>5.2</td>\n",
       "      <td>3.7</td>\n",
       "      <td>12.1</td>\n",
       "      <td>27.0</td>\n",
       "      <td>368,288,939,768.3</td>\n",
       "      <td>None</td>\n",
       "      <td>-11,640,181,654.7</td>\n",
       "      <td>8,014,039,701.1</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2018</td>\n",
       "      <td>South Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>57,000,451.0</td>\n",
       "      <td>28,883,601.0</td>\n",
       "      <td>28,116,850.0</td>\n",
       "      <td>20.9</td>\n",
       "      <td>9.5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>5.2</td>\n",
       "      <td>3.7</td>\n",
       "      <td>12.2</td>\n",
       "      <td>27.3</td>\n",
       "      <td>349,554,116,683.8</td>\n",
       "      <td>0.8</td>\n",
       "      <td>-10,483,365,758.0</td>\n",
       "      <td>8,253,798,946.9</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2017</td>\n",
       "      <td>South Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>56,203,654.0</td>\n",
       "      <td>28,469,912.0</td>\n",
       "      <td>27,733,742.0</td>\n",
       "      <td>21.3</td>\n",
       "      <td>9.6</td>\n",
       "      <td>9.0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>5.6</td>\n",
       "      <td>3.9</td>\n",
       "      <td>11.9</td>\n",
       "      <td>26.6</td>\n",
       "      <td>296,357,282,715.1</td>\n",
       "      <td>-1.7</td>\n",
       "      <td>-8,190,335,037.5</td>\n",
       "      <td>6,591,156,314.6</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2016</td>\n",
       "      <td>South Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>55,386,367.0</td>\n",
       "      <td>28,049,364.0</td>\n",
       "      <td>27,337,003.0</td>\n",
       "      <td>21.7</td>\n",
       "      <td>9.8</td>\n",
       "      <td>9.0</td>\n",
       "      <td>23.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>4.2</td>\n",
       "      <td>11.8</td>\n",
       "      <td>25.2</td>\n",
       "      <td>317,620,522,794.8</td>\n",
       "      <td>2.3</td>\n",
       "      <td>-7,871,410,981.4</td>\n",
       "      <td>6,634,302,430.5</td>\n",
       "      <td>None</td>\n",
       "      <td>17.2</td>\n",
       "      <td>None</td>\n",
       "      <td>2015</td>\n",
       "      <td>South Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>54,545,991.0</td>\n",
       "      <td>27,621,049.0</td>\n",
       "      <td>26,924,942.0</td>\n",
       "      <td>22.1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>23.4</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>12.1</td>\n",
       "      <td>24.9</td>\n",
       "      <td>350,904,575,292.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-9,363,726,900.5</td>\n",
       "      <td>7,631,145,522.1</td>\n",
       "      <td>4,197.9</td>\n",
       "      <td>16.6</td>\n",
       "      <td>86.8</td>\n",
       "      <td>2014</td>\n",
       "      <td>South Africa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Total Population Female Population Male Population Birth Rate Death Rate  \\\n",
       "0     57,779,622.0      29,290,888.0    28,488,734.0       None       None   \n",
       "1     57,000,451.0      28,883,601.0    28,116,850.0       20.9        9.5   \n",
       "2     56,203,654.0      28,469,912.0    27,733,742.0       21.3        9.6   \n",
       "3     55,386,367.0      28,049,364.0    27,337,003.0       21.7        9.8   \n",
       "4     54,545,991.0      27,621,049.0    26,924,942.0       22.1       10.0   \n",
       "\n",
       "  Compulsory Education Dur. Employment in Industry(%)  \\\n",
       "0                       9.0                      23.2   \n",
       "1                       9.0                      23.3   \n",
       "2                       9.0                      23.3   \n",
       "3                       9.0                      23.8   \n",
       "4                       9.0                      23.4   \n",
       "\n",
       "  Employment in Agriculture(%) Female Employment in Agriculture(%)  \\\n",
       "0                          5.2                                 3.7   \n",
       "1                          5.2                                 3.7   \n",
       "2                          5.6                                 3.9   \n",
       "3                          5.6                                 4.2   \n",
       "4                          4.7                                 3.2   \n",
       "\n",
       "  Female Employment in Industry(%) Unemployment(%)        GDP in USD  \\\n",
       "0                             12.1            27.0 368,288,939,768.3   \n",
       "1                             12.2            27.3 349,554,116,683.8   \n",
       "2                             11.9            26.6 296,357,282,715.1   \n",
       "3                             11.8            25.2 317,620,522,794.8   \n",
       "4                             12.1            24.9 350,904,575,292.3   \n",
       "\n",
       "  National Income per Capita Net income from Abroad  \\\n",
       "0                       None      -11,640,181,654.7   \n",
       "1                        0.8      -10,483,365,758.0   \n",
       "2                       -1.7       -8,190,335,037.5   \n",
       "3                        2.3       -7,871,410,981.4   \n",
       "4                        0.5       -9,363,726,900.5   \n",
       "\n",
       "  Agriculture value added(in USD) Electric Power Consumption(kWH per capita)  \\\n",
       "0                 8,014,039,701.1                                       None   \n",
       "1                 8,253,798,946.9                                       None   \n",
       "2                 6,591,156,314.6                                       None   \n",
       "3                 6,634,302,430.5                                       None   \n",
       "4                 7,631,145,522.1                                    4,197.9   \n",
       "\n",
       "  Renewable Energy Consumption (%) Fossil Fuel Consumption (%)  Year  \\\n",
       "0                             None                        None  2018   \n",
       "1                             None                        None  2017   \n",
       "2                             None                        None  2016   \n",
       "3                             17.2                        None  2015   \n",
       "4                             16.6                        86.8  2014   \n",
       "\n",
       "        Country  \n",
       "0  South Africa  \n",
       "1  South Africa  \n",
       "2  South Africa  \n",
       "3  South Africa  \n",
       "4  South Africa  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data Loading Completed\n"
     ]
    }
   ],
   "source": [
    "# Call the getCountrywiseDF function with the code of each country under consideration\n",
    "# We will have a seperate dataframe for each country - 7 data frames\n",
    "\n",
    "US_df=getCountrywiseDF('US')\n",
    "IN_df=getCountrywiseDF('IN')\n",
    "CN_df=getCountrywiseDF('CN')\n",
    "JP_df=getCountrywiseDF('JP')\n",
    "CA_df=getCountrywiseDF('CA')\n",
    "GB_df=getCountrywiseDF('GB')\n",
    "ZA_df=getCountrywiseDF('ZA')\n",
    "\n",
    "print(\"Data Loading Completed\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task-3: Data Pre-processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above data has just been collected through multiple API calls and combined together. As we can see from the dataframes that we created by fetching the features from the API calls, contain some missing values in some of the features. This means that the data needs some processing before using it for analysis.\n",
    "\n",
    "For easing the task of pre-processing and avoiding manually passing the dataframes to each of the following functions, we create a list of the dataframes created. The copy() method has been used to avoid changing the original unprocessed dataframes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "# store all the DataFrames in a list to iteratively apply pre-processing steps\n",
    "list_df=[US_df.copy(), IN_df.copy(), CN_df.copy(), JP_df.copy(), CA_df.copy(), GB_df.copy(), ZA_df.copy()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Drop features with majority values missing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to identify the features with a large number of missing values. Such features are not useful for analysis and can be removed from the dataset.\n",
    "The following function has been implemented to perform this task.\n",
    "\n",
    "* **remove_missing_features():** This function takes a dataframe as a parameter. It finds the features that contain non-zero missing values. It then finds the percentage of missing values in each of the columns. The percentage of missing values is checked and if it is greater than 75%, the column is dropped from the dataframe and an updated dataframe is returned. If a column has more than 75% missing values it is absolutely not useful for analysis. This function is iteratively called on all of the dataframes created above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to identify missing features and remove features that aren't useful\n",
    "\n",
    "def remove_missing_features(df):\n",
    "    \n",
    "    # validation for dataframe\n",
    "    if df is None:\n",
    "        print(\"No DataFrame received!\")\n",
    "        return\n",
    "    \n",
    "    # create a copy of the dataframe to avoid changing the original\n",
    "    df_cp=df.copy()\n",
    "    \n",
    "    print(\"Removing missing features for: \" + df_cp.iloc[0]['Country'])\n",
    "    \n",
    "    # find features with non-zero missing values\n",
    "    n_missing_vals=df.isnull().sum()\n",
    "\n",
    "    # get the index list of the features with non-zero missing values\n",
    "    n_missing_index_list = list(n_missing_vals.index)\n",
    "    \n",
    "    # calculate the percentage of missing values\n",
    "    # shape[0] gives the number of rows in the dataframe, hence, by diving the no. number of missing values by the total\n",
    "    # no. of rows we get the ratio of missing values - multipled by 100 to get percentage\n",
    "    # here missing_percentage consists of key value pairs - column name: percentage of missing values\n",
    "    missing_percentage = n_missing_vals[n_missing_vals!=0]/df.shape[0]*100\n",
    "    \n",
    "    '''\n",
    "    Example: missing_percentage\n",
    "    Total Population 1.694915254237288\n",
    "    Female Population 1.694915254237288\n",
    "    Male Population 83.05084745762711\n",
    "    Birth Rate 52.54237288135594\n",
    "    '''\n",
    "\n",
    "    # list to maintain the columns to drop\n",
    "    cols_to_trim=[]\n",
    "    \n",
    "    # iterate over each key value pair\n",
    "    for i,val in enumerate(missing_percentage):\n",
    "        # if percentage value is > 75\n",
    "        if val > 75:\n",
    "            # add the corresponding column to the list of cols_to_trim\n",
    "            cols_to_trim.append(n_missing_index_list[i])\n",
    "\n",
    "    # Example: cols_to_trim=['Male Population']\n",
    "\n",
    "    if len(cols_to_trim) > 0:\n",
    "        # drop the columns identified using the dataframe drop() method\n",
    "        df_cp=df_cp.drop(columns=cols_to_trim)\n",
    "        print(\"Dropped Columns:\" + str(cols_to_trim))\n",
    "    else:\n",
    "        print(\"No columns dropped\")\n",
    "\n",
    "    # return the updated dataframe\n",
    "    return df_cp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removing missing features for: USA\n",
      "No columns dropped\n",
      "Removing missing features for: India\n",
      "Dropped Columns:['Male Population']\n",
      "Removing missing features for: China\n",
      "No columns dropped\n",
      "Removing missing features for: Japan\n",
      "No columns dropped\n",
      "Removing missing features for: Canada\n",
      "No columns dropped\n",
      "Removing missing features for: Great Britain\n",
      "No columns dropped\n",
      "Removing missing features for: South Africa\n",
      "No columns dropped\n"
     ]
    }
   ],
   "source": [
    "# call the function on each DF for each country.\n",
    "# The function remove_missing_features will be applied on each dataframe in list_df through the map function in python.\n",
    "list_df=list(map(remove_missing_features, list_df))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the only column dropped was Male population from the country India.\n",
    "For rest of the countries, all the features consisted of less than 75% missing values. So far we have only removed features that had missing values greater than a certain threshold. This means that, some features may still contain missing values. The next task is to fill these missing values so that they can be used in analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Fill missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following function has been implemented to fill the missing values in features:\n",
    "* **fill_missing_values():** This function takes a dataframe in which the missing values need to be filled. It uses the *fillna()* function of pandas dataframes to fill values that are NaNs. As observed from the raw dataframes populated above, the missing values are denoted by None. Hence we first fill NaN in place of None and then replace the NaNs with the mean value of the columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to fill the remaining missing values with average values for columns\n",
    "\n",
    "def fill_missing_values(df):\n",
    "    \n",
    "    # validation for dataframes\n",
    "    if df is None:\n",
    "        print(\"No DataFrame received\")\n",
    "        return\n",
    "\n",
    "    # create a copy\n",
    "    df_cp=df.copy()\n",
    "    \n",
    "    print(\"Filling missing features for: \" + df_cp.iloc[0]['Country'])\n",
    "    \n",
    "    # get the list of columns in the dataframe\n",
    "    cols_list=list(df_cp.columns)\n",
    "    \n",
    "    # exclude the last column - Country\n",
    "    # This column was added explicitly when the data was loaded, hence, it does not contain any missing values.\n",
    "    # Also, fillna function does not work on categorical features since it performs an aggregation.\n",
    "    cols_list.pop()\n",
    "    \n",
    "    # replace all None values with NaN, fillna only works on nans\n",
    "    df_cp.fillna(value=pd.np.nan, inplace=True)\n",
    "    \n",
    "    # replace all NaN values with the mean of the column values\n",
    "    for col in cols_list:\n",
    "        df_cp[col].fillna((df_cp[col].mean()), inplace=True)\n",
    "\n",
    "    print(\"Filling missing values completed\")\n",
    "    return df_cp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filling missing features for: USA\n",
      "Filling missing values completed\n",
      "Filling missing features for: India\n",
      "Filling missing values completed\n",
      "Filling missing features for: China\n",
      "Filling missing values completed\n",
      "Filling missing features for: Japan\n",
      "Filling missing values completed\n",
      "Filling missing features for: Canada\n",
      "Filling missing values completed\n",
      "Filling missing features for: Great Britain\n",
      "Filling missing values completed\n",
      "Filling missing features for: South Africa\n",
      "Filling missing values completed\n"
     ]
    }
   ],
   "source": [
    "# call the function on each DF for each country.\n",
    "# The function fill_missing_features will be applied on each dataframe in list_df through the map function in python.\n",
    "\n",
    "list_df=list(map(fill_missing_values, list_df))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Change the type of Numeric but Categorical Features\n",
    "\n",
    "We can observe that the 'Year' column is a numeric value, however, it's magnitude does not have a significance. It actually represents a period and not an actual number. Hence, this column can be converted to a categorical variable.\n",
    "The following function has been implemented to achieve this:\n",
    "\n",
    "* **change_year_type():** This function takes a dataframe as a parameter and changes the dtype of year and returns the updated dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to change year type\n",
    "def change_year_type(df):\n",
    "    \n",
    "    print(\"Changing type of Year for: \" + df.loc[0]['Country'])\n",
    "    # validation to check if year column exists in the dataframe\n",
    "    if 'Year' in df.columns:\n",
    "        # convert year to a string\n",
    "        df['Year'] = df.Year.astype(str)\n",
    "    \n",
    "    print(\"Completed changing type\")\n",
    "    # return the updated df\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Changing type of Year for: USA\n",
      "Completed changing type\n",
      "Changing type of Year for: India\n",
      "Completed changing type\n",
      "Changing type of Year for: China\n",
      "Completed changing type\n",
      "Changing type of Year for: Japan\n",
      "Completed changing type\n",
      "Changing type of Year for: Canada\n",
      "Completed changing type\n",
      "Changing type of Year for: Great Britain\n",
      "Completed changing type\n",
      "Changing type of Year for: South Africa\n",
      "Completed changing type\n"
     ]
    }
   ],
   "source": [
    "# call the function on each DF for each country.\n",
    "# The function fill_missing_features will be applied on each dataframe in list_df through the map function in python.\n",
    "\n",
    "list_df=list(map(change_year_type, list_df))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Derive deleted features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In one of the above steps, we can see that the column **Male Population** from the DF for country **India** was dropped since it had too many missing values. This feature may be useful in further analysis. Also, since rest of the DataFrames contain this column, to maintain consistency, we can derive this feature from Total Population and Female population. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total Population</th>\n",
       "      <th>Female Population</th>\n",
       "      <th>Birth Rate</th>\n",
       "      <th>Death Rate</th>\n",
       "      <th>Compulsory Education Dur.</th>\n",
       "      <th>Employment in Industry(%)</th>\n",
       "      <th>Employment in Agriculture(%)</th>\n",
       "      <th>Female Employment in Agriculture(%)</th>\n",
       "      <th>Female Employment in Industry(%)</th>\n",
       "      <th>Unemployment(%)</th>\n",
       "      <th>GDP in USD</th>\n",
       "      <th>National Income per Capita</th>\n",
       "      <th>Net income from Abroad</th>\n",
       "      <th>Agriculture value added(in USD)</th>\n",
       "      <th>Electric Power Consumption(kWH per capita)</th>\n",
       "      <th>Renewable Energy Consumption (%)</th>\n",
       "      <th>Fossil Fuel Consumption (%)</th>\n",
       "      <th>Year</th>\n",
       "      <th>Country</th>\n",
       "      <th>Male Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1,352,617,328.0</td>\n",
       "      <td>649,574,719.0</td>\n",
       "      <td>31.3</td>\n",
       "      <td>12.3</td>\n",
       "      <td>8.0</td>\n",
       "      <td>24.7</td>\n",
       "      <td>43.9</td>\n",
       "      <td>57.1</td>\n",
       "      <td>18.7</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2,718,732,231,257.6</td>\n",
       "      <td>3.5</td>\n",
       "      <td>-27,691,371,968.3</td>\n",
       "      <td>396,987,486,510.1</td>\n",
       "      <td>335.2</td>\n",
       "      <td>48.4</td>\n",
       "      <td>54.7</td>\n",
       "      <td>2018</td>\n",
       "      <td>India</td>\n",
       "      <td>703,042,609.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1,338,658,835.0</td>\n",
       "      <td>642,787,641.0</td>\n",
       "      <td>18.1</td>\n",
       "      <td>7.2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>24.5</td>\n",
       "      <td>44.5</td>\n",
       "      <td>57.6</td>\n",
       "      <td>18.7</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2,652,242,857,923.9</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-28,673,153,563.2</td>\n",
       "      <td>414,265,878,403.3</td>\n",
       "      <td>335.2</td>\n",
       "      <td>48.4</td>\n",
       "      <td>54.7</td>\n",
       "      <td>2017</td>\n",
       "      <td>India</td>\n",
       "      <td>695,871,194.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1,324,509,589.0</td>\n",
       "      <td>635,908,885.0</td>\n",
       "      <td>18.3</td>\n",
       "      <td>7.2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>24.3</td>\n",
       "      <td>45.1</td>\n",
       "      <td>58.2</td>\n",
       "      <td>18.6</td>\n",
       "      <td>2.7</td>\n",
       "      <td>2,290,432,075,123.8</td>\n",
       "      <td>6.4</td>\n",
       "      <td>-26,300,115,592.2</td>\n",
       "      <td>372,190,852,855.1</td>\n",
       "      <td>335.2</td>\n",
       "      <td>48.4</td>\n",
       "      <td>54.7</td>\n",
       "      <td>2016</td>\n",
       "      <td>India</td>\n",
       "      <td>688,600,704.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1,310,152,403.0</td>\n",
       "      <td>628,929,065.0</td>\n",
       "      <td>18.6</td>\n",
       "      <td>7.2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>24.3</td>\n",
       "      <td>45.6</td>\n",
       "      <td>58.6</td>\n",
       "      <td>18.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>2,103,587,813,812.8</td>\n",
       "      <td>7.6</td>\n",
       "      <td>-24,405,488,068.3</td>\n",
       "      <td>340,244,981,475.1</td>\n",
       "      <td>335.2</td>\n",
       "      <td>36.0</td>\n",
       "      <td>54.7</td>\n",
       "      <td>2015</td>\n",
       "      <td>India</td>\n",
       "      <td>681,223,338.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1,295,604,184.0</td>\n",
       "      <td>621,854,638.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>24.4</td>\n",
       "      <td>46.1</td>\n",
       "      <td>59.1</td>\n",
       "      <td>18.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>2,039,127,446,298.6</td>\n",
       "      <td>5.8</td>\n",
       "      <td>-24,112,069,639.3</td>\n",
       "      <td>342,408,945,451.4</td>\n",
       "      <td>804.5</td>\n",
       "      <td>36.7</td>\n",
       "      <td>73.6</td>\n",
       "      <td>2014</td>\n",
       "      <td>India</td>\n",
       "      <td>673,749,546.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total Population  Female Population  Birth Rate  Death Rate  \\\n",
       "0   1,352,617,328.0      649,574,719.0        31.3        12.3   \n",
       "1   1,338,658,835.0      642,787,641.0        18.1         7.2   \n",
       "2   1,324,509,589.0      635,908,885.0        18.3         7.2   \n",
       "3   1,310,152,403.0      628,929,065.0        18.6         7.2   \n",
       "4   1,295,604,184.0      621,854,638.0        19.0         7.2   \n",
       "\n",
       "   Compulsory Education Dur.  Employment in Industry(%)  \\\n",
       "0                        8.0                       24.7   \n",
       "1                        8.0                       24.5   \n",
       "2                        8.0                       24.3   \n",
       "3                        8.0                       24.3   \n",
       "4                        8.0                       24.4   \n",
       "\n",
       "   Employment in Agriculture(%)  Female Employment in Agriculture(%)  \\\n",
       "0                          43.9                                 57.1   \n",
       "1                          44.5                                 57.6   \n",
       "2                          45.1                                 58.2   \n",
       "3                          45.6                                 58.6   \n",
       "4                          46.1                                 59.1   \n",
       "\n",
       "   Female Employment in Industry(%)  Unemployment(%)          GDP in USD  \\\n",
       "0                              18.7              2.6 2,718,732,231,257.6   \n",
       "1                              18.7              2.6 2,652,242,857,923.9   \n",
       "2                              18.6              2.7 2,290,432,075,123.8   \n",
       "3                              18.7              2.8 2,103,587,813,812.8   \n",
       "4                              18.7              2.8 2,039,127,446,298.6   \n",
       "\n",
       "   National Income per Capita  Net income from Abroad  \\\n",
       "0                         3.5       -27,691,371,968.3   \n",
       "1                         4.0       -28,673,153,563.2   \n",
       "2                         6.4       -26,300,115,592.2   \n",
       "3                         7.6       -24,405,488,068.3   \n",
       "4                         5.8       -24,112,069,639.3   \n",
       "\n",
       "   Agriculture value added(in USD)  \\\n",
       "0                396,987,486,510.1   \n",
       "1                414,265,878,403.3   \n",
       "2                372,190,852,855.1   \n",
       "3                340,244,981,475.1   \n",
       "4                342,408,945,451.4   \n",
       "\n",
       "   Electric Power Consumption(kWH per capita)  \\\n",
       "0                                       335.2   \n",
       "1                                       335.2   \n",
       "2                                       335.2   \n",
       "3                                       335.2   \n",
       "4                                       804.5   \n",
       "\n",
       "   Renewable Energy Consumption (%)  Fossil Fuel Consumption (%)  Year  \\\n",
       "0                              48.4                         54.7  2018   \n",
       "1                              48.4                         54.7  2017   \n",
       "2                              48.4                         54.7  2016   \n",
       "3                              36.0                         54.7  2015   \n",
       "4                              36.7                         73.6  2014   \n",
       "\n",
       "  Country  Male Population  \n",
       "0   India    703,042,609.0  \n",
       "1   India    695,871,194.0  \n",
       "2   India    688,600,704.0  \n",
       "3   India    681,223,338.0  \n",
       "4   India    673,749,546.0  "
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pick the dataframe for the country India from the list of dataframes\n",
    "dfIndia = list_df[1]\n",
    "\n",
    "# Male Population in any given year=Total Population - Female Population\n",
    "# validation so that the operation does not fail\n",
    "if 'Total Population' in dfIndia.columns and 'Female Population' in dfIndia.columns:\n",
    "    # calculate\n",
    "    dfIndia[\"Male Population\"] = dfIndia[\"Total Population\"] - dfIndia[\"Female Population\"]\n",
    "else:\n",
    "    # print error message\n",
    "    print(\"One of the columns Total Population or Female Population is missing.\")\n",
    "\n",
    "list_df[1].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have now completed all our preprocessing steps and have 7 dataframes for the 7 countries under consideration. Let us now examine the number of features and and their datatypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check the number of features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of features: 20\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Total Population                              float64\n",
       "Female Population                             float64\n",
       "Male Population                               float64\n",
       "Birth Rate                                    float64\n",
       "Death Rate                                    float64\n",
       "Compulsory Education Dur.                     float64\n",
       "Employment in Industry(%)                     float64\n",
       "Employment in Agriculture(%)                  float64\n",
       "Female Employment in Agriculture(%)           float64\n",
       "Female Employment in Industry(%)              float64\n",
       "Unemployment(%)                               float64\n",
       "GDP in USD                                    float64\n",
       "National Income per Capita                    float64\n",
       "Net income from Abroad                        float64\n",
       "Agriculture value added(in USD)               float64\n",
       "Electric Power Consumption(kWH per capita)    float64\n",
       "Renewable Energy Consumption (%)              float64\n",
       "Fossil Fuel Consumption (%)                   float64\n",
       "Year                                           object\n",
       "Country                                        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# each dataframe has the same number of columns. Hence, we only check the first dataframe in the list\n",
    "\n",
    "print('Total number of features: %d\\n'%(list_df[0].shape[1]))\n",
    "list_df[0].dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now have a total of 20 features to work on. We can see that all of the features are numeric except for Year and Country which are categorical"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Store the cleaned dataset into CSV files\n",
    "\n",
    "The dataframes for the 7 countries are stored into different CSV files for further analysis. The following function has been implemented for this task:\n",
    "* **write_data():** Iterates over the list of dataframes and writes them to CSV files with the name of the country. For example, the file containing data for India is stored in the file India.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to write processed data to CSV files\n",
    "def write_data():\n",
    "    # validation to check if the number of countries and the dataframes match\n",
    "    assert len(list(countryMap.keys()))==len(list_df)\n",
    "    \n",
    "    # iterate over country names from the country map and the list of dataframes simultaneously\n",
    "    for country_name, df_data in zip(COUNTRY_LIST, list_df):\n",
    "        print(\"Writing data for: \" + country_name)\n",
    "        file_name=country_name+\".csv\"\n",
    "        # convert to CSV\n",
    "        try:\n",
    "            df_data.to_csv(file_name, index=False)\n",
    "            print(\"Successfully created: \" + file_name)\n",
    "        except:\n",
    "            # in case an error occurs in I/O\n",
    "            print(\"Error in writing to: \" + file_name)\n",
    "        \n",
    "        # Note that the order in which the countries have been maintained in the country_list\n",
    "        # and the order in which they have been maintained is the same"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing data for: USA\n",
      "Successfully created: USA.csv\n",
      "Writing data for: India\n",
      "Successfully created: India.csv\n",
      "Writing data for: China\n",
      "Successfully created: China.csv\n",
      "Writing data for: Japan\n",
      "Successfully created: Japan.csv\n",
      "Writing data for: Canada\n",
      "Successfully created: Canada.csv\n",
      "Writing data for: Great Britain\n",
      "Successfully created: Great Britain.csv\n",
      "Writing data for: South Africa\n",
      "Successfully created: South Africa.csv\n"
     ]
    }
   ],
   "source": [
    "# call the function\n",
    "write_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task 5: Analyse and Summarise the cleaned dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we have all the pre-processed datasets, we can create visualisations from them to identify patterns. I have used the **matplotlib** and **seaborn** libraries to analyse the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read the cleaned data from the CSV files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now use the cleaned dataset for analysis. Hence we first read the CSV files we created in the previous task."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully read all the files\n",
      "Displaying data for USA: \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total Population</th>\n",
       "      <th>Female Population</th>\n",
       "      <th>Male Population</th>\n",
       "      <th>Birth Rate</th>\n",
       "      <th>Death Rate</th>\n",
       "      <th>Compulsory Education Dur.</th>\n",
       "      <th>Employment in Industry(%)</th>\n",
       "      <th>Employment in Agriculture(%)</th>\n",
       "      <th>Female Employment in Agriculture(%)</th>\n",
       "      <th>Female Employment in Industry(%)</th>\n",
       "      <th>Unemployment(%)</th>\n",
       "      <th>GDP in USD</th>\n",
       "      <th>National Income per Capita</th>\n",
       "      <th>Net income from Abroad</th>\n",
       "      <th>Agriculture value added(in USD)</th>\n",
       "      <th>Electric Power Consumption(kWH per capita)</th>\n",
       "      <th>Renewable Energy Consumption (%)</th>\n",
       "      <th>Fossil Fuel Consumption (%)</th>\n",
       "      <th>Year</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>327,167,434.0</td>\n",
       "      <td>165,285,036.0</td>\n",
       "      <td>161,882,398.0</td>\n",
       "      <td>15.7</td>\n",
       "      <td>8.7</td>\n",
       "      <td>12.0</td>\n",
       "      <td>19.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.8</td>\n",
       "      <td>8.5</td>\n",
       "      <td>3.9</td>\n",
       "      <td>20,544,343,456,936.5</td>\n",
       "      <td>1.7</td>\n",
       "      <td>267,900,000,000.0</td>\n",
       "      <td>152,778,873,809.5</td>\n",
       "      <td>10,318.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>89.3</td>\n",
       "      <td>2018</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>325,147,121.0</td>\n",
       "      <td>164,275,323.0</td>\n",
       "      <td>160,871,798.0</td>\n",
       "      <td>11.8</td>\n",
       "      <td>8.5</td>\n",
       "      <td>12.0</td>\n",
       "      <td>19.7</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.8</td>\n",
       "      <td>8.7</td>\n",
       "      <td>4.4</td>\n",
       "      <td>19,485,393,853,000.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>243,296,000,000.0</td>\n",
       "      <td>178,579,605,000.0</td>\n",
       "      <td>10,318.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>89.3</td>\n",
       "      <td>2017</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>323,071,342.0</td>\n",
       "      <td>163,245,729.0</td>\n",
       "      <td>159,825,613.0</td>\n",
       "      <td>12.2</td>\n",
       "      <td>8.5</td>\n",
       "      <td>12.0</td>\n",
       "      <td>19.8</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.8</td>\n",
       "      <td>8.7</td>\n",
       "      <td>4.9</td>\n",
       "      <td>18,707,188,235,000.0</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>218,168,000,000.0</td>\n",
       "      <td>175,923,170,000.0</td>\n",
       "      <td>10,318.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>89.3</td>\n",
       "      <td>2016</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>320,742,673.0</td>\n",
       "      <td>162,097,641.0</td>\n",
       "      <td>158,645,032.0</td>\n",
       "      <td>12.4</td>\n",
       "      <td>8.4</td>\n",
       "      <td>12.0</td>\n",
       "      <td>19.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.7</td>\n",
       "      <td>8.8</td>\n",
       "      <td>5.3</td>\n",
       "      <td>18,219,297,584,000.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>224,626,000,000.0</td>\n",
       "      <td>189,947,276,000.0</td>\n",
       "      <td>10,318.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>82.4</td>\n",
       "      <td>2015</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>318,386,421.0</td>\n",
       "      <td>160,946,480.0</td>\n",
       "      <td>157,439,941.0</td>\n",
       "      <td>12.5</td>\n",
       "      <td>8.2</td>\n",
       "      <td>12.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.7</td>\n",
       "      <td>8.9</td>\n",
       "      <td>6.2</td>\n",
       "      <td>17,521,746,534,000.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>241,062,000,000.0</td>\n",
       "      <td>208,273,224,000.0</td>\n",
       "      <td>12,994.0</td>\n",
       "      <td>8.8</td>\n",
       "      <td>83.1</td>\n",
       "      <td>2014</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total Population  Female Population  Male Population  Birth Rate  \\\n",
       "0     327,167,434.0      165,285,036.0    161,882,398.0        15.7   \n",
       "1     325,147,121.0      164,275,323.0    160,871,798.0        11.8   \n",
       "2     323,071,342.0      163,245,729.0    159,825,613.0        12.2   \n",
       "3     320,742,673.0      162,097,641.0    158,645,032.0        12.4   \n",
       "4     318,386,421.0      160,946,480.0    157,439,941.0        12.5   \n",
       "\n",
       "   Death Rate  Compulsory Education Dur.  Employment in Industry(%)  \\\n",
       "0         8.7                       12.0                       19.4   \n",
       "1         8.5                       12.0                       19.7   \n",
       "2         8.5                       12.0                       19.8   \n",
       "3         8.4                       12.0                       19.9   \n",
       "4         8.2                       12.0                       20.0   \n",
       "\n",
       "   Employment in Agriculture(%)  Female Employment in Agriculture(%)  \\\n",
       "0                           1.4                                  0.8   \n",
       "1                           1.4                                  0.8   \n",
       "2                           1.4                                  0.8   \n",
       "3                           1.4                                  0.7   \n",
       "4                           1.4                                  0.7   \n",
       "\n",
       "   Female Employment in Industry(%)  Unemployment(%)           GDP in USD  \\\n",
       "0                               8.5              3.9 20,544,343,456,936.5   \n",
       "1                               8.7              4.4 19,485,393,853,000.0   \n",
       "2                               8.7              4.9 18,707,188,235,000.0   \n",
       "3                               8.8              5.3 18,219,297,584,000.0   \n",
       "4                               8.9              6.2 17,521,746,534,000.0   \n",
       "\n",
       "   National Income per Capita  Net income from Abroad  \\\n",
       "0                         1.7       267,900,000,000.0   \n",
       "1                         2.2       243,296,000,000.0   \n",
       "2                        -0.1       218,168,000,000.0   \n",
       "3                         2.4       224,626,000,000.0   \n",
       "4                         2.9       241,062,000,000.0   \n",
       "\n",
       "   Agriculture value added(in USD)  \\\n",
       "0                152,778,873,809.5   \n",
       "1                178,579,605,000.0   \n",
       "2                175,923,170,000.0   \n",
       "3                189,947,276,000.0   \n",
       "4                208,273,224,000.0   \n",
       "\n",
       "   Electric Power Consumption(kWH per capita)  \\\n",
       "0                                    10,318.0   \n",
       "1                                    10,318.0   \n",
       "2                                    10,318.0   \n",
       "3                                    10,318.0   \n",
       "4                                    12,994.0   \n",
       "\n",
       "   Renewable Energy Consumption (%)  Fossil Fuel Consumption (%)  Year Country  \n",
       "0                               6.0                         89.3  2018     USA  \n",
       "1                               6.0                         89.3  2017     USA  \n",
       "2                               6.0                         89.3  2016     USA  \n",
       "3                               8.7                         82.4  2015     USA  \n",
       "4                               8.8                         83.1  2014     USA  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# read the cleaned data from every CSV\n",
    "try:\n",
    "    df_cleaned_us=pd.read_csv('USA.csv')\n",
    "    df_cleaned_in=pd.read_csv('India.csv')\n",
    "    df_cleaned_cn=pd.read_csv('China.csv')\n",
    "    df_cleaned_jp=pd.read_csv('Japan.csv')\n",
    "    df_cleaned_ca=pd.read_csv('Canada.csv')\n",
    "    df_cleaned_gb=pd.read_csv('Great Britain.csv')\n",
    "    df_cleaned_za=pd.read_csv('South Africa.csv')\n",
    "    print(\"Successfully read all the files\")\n",
    "except:\n",
    "    # handling I/O exceptions\n",
    "    print(\"Unexpected error in reading a file. Check the file path and if a file exists with the name given.\")\n",
    "\n",
    "# display data of one country to check if the cleaned data is loaded\n",
    "print(\"Displaying data for USA: \")\n",
    "display(df_cleaned_us.head())\n",
    "\n",
    "# create a list of clean dataframes for further analysis\n",
    "list_cleaned_df=[df_cleaned_us, df_cleaned_in, df_cleaned_cn, df_cleaned_jp, df_cleaned_ca, df_cleaned_gb, df_cleaned_za]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare a combined DataFrame for further analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For each country, separate dataframes have been created. There may be cases when we may need the entire data across all countries in certain analysis. Hence it is a good idea to have a single combined dataframe ready."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total Population</th>\n",
       "      <th>Female Population</th>\n",
       "      <th>Male Population</th>\n",
       "      <th>Birth Rate</th>\n",
       "      <th>Death Rate</th>\n",
       "      <th>Compulsory Education Dur.</th>\n",
       "      <th>Employment in Industry(%)</th>\n",
       "      <th>Employment in Agriculture(%)</th>\n",
       "      <th>Female Employment in Agriculture(%)</th>\n",
       "      <th>Female Employment in Industry(%)</th>\n",
       "      <th>Unemployment(%)</th>\n",
       "      <th>GDP in USD</th>\n",
       "      <th>National Income per Capita</th>\n",
       "      <th>Net income from Abroad</th>\n",
       "      <th>Agriculture value added(in USD)</th>\n",
       "      <th>Electric Power Consumption(kWH per capita)</th>\n",
       "      <th>Renewable Energy Consumption (%)</th>\n",
       "      <th>Fossil Fuel Consumption (%)</th>\n",
       "      <th>Year</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>327,167,434.0</td>\n",
       "      <td>165,285,036.0</td>\n",
       "      <td>161,882,398.0</td>\n",
       "      <td>15.7</td>\n",
       "      <td>8.7</td>\n",
       "      <td>12.0</td>\n",
       "      <td>19.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.8</td>\n",
       "      <td>8.5</td>\n",
       "      <td>3.9</td>\n",
       "      <td>20,544,343,456,936.5</td>\n",
       "      <td>1.7</td>\n",
       "      <td>267,900,000,000.0</td>\n",
       "      <td>152,778,873,809.5</td>\n",
       "      <td>10,318.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>89.3</td>\n",
       "      <td>2018</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>325,147,121.0</td>\n",
       "      <td>164,275,323.0</td>\n",
       "      <td>160,871,798.0</td>\n",
       "      <td>11.8</td>\n",
       "      <td>8.5</td>\n",
       "      <td>12.0</td>\n",
       "      <td>19.7</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.8</td>\n",
       "      <td>8.7</td>\n",
       "      <td>4.4</td>\n",
       "      <td>19,485,393,853,000.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>243,296,000,000.0</td>\n",
       "      <td>178,579,605,000.0</td>\n",
       "      <td>10,318.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>89.3</td>\n",
       "      <td>2017</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>323,071,342.0</td>\n",
       "      <td>163,245,729.0</td>\n",
       "      <td>159,825,613.0</td>\n",
       "      <td>12.2</td>\n",
       "      <td>8.5</td>\n",
       "      <td>12.0</td>\n",
       "      <td>19.8</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.8</td>\n",
       "      <td>8.7</td>\n",
       "      <td>4.9</td>\n",
       "      <td>18,707,188,235,000.0</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>218,168,000,000.0</td>\n",
       "      <td>175,923,170,000.0</td>\n",
       "      <td>10,318.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>89.3</td>\n",
       "      <td>2016</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>320,742,673.0</td>\n",
       "      <td>162,097,641.0</td>\n",
       "      <td>158,645,032.0</td>\n",
       "      <td>12.4</td>\n",
       "      <td>8.4</td>\n",
       "      <td>12.0</td>\n",
       "      <td>19.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.7</td>\n",
       "      <td>8.8</td>\n",
       "      <td>5.3</td>\n",
       "      <td>18,219,297,584,000.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>224,626,000,000.0</td>\n",
       "      <td>189,947,276,000.0</td>\n",
       "      <td>10,318.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>82.4</td>\n",
       "      <td>2015</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>318,386,421.0</td>\n",
       "      <td>160,946,480.0</td>\n",
       "      <td>157,439,941.0</td>\n",
       "      <td>12.5</td>\n",
       "      <td>8.2</td>\n",
       "      <td>12.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.7</td>\n",
       "      <td>8.9</td>\n",
       "      <td>6.2</td>\n",
       "      <td>17,521,746,534,000.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>241,062,000,000.0</td>\n",
       "      <td>208,273,224,000.0</td>\n",
       "      <td>12,994.0</td>\n",
       "      <td>8.8</td>\n",
       "      <td>83.1</td>\n",
       "      <td>2014</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>126,843,000.0</td>\n",
       "      <td>64,552,936.0</td>\n",
       "      <td>62,290,064.0</td>\n",
       "      <td>9.4</td>\n",
       "      <td>7.7</td>\n",
       "      <td>9.0</td>\n",
       "      <td>31.4</td>\n",
       "      <td>5.1</td>\n",
       "      <td>5.5</td>\n",
       "      <td>21.6</td>\n",
       "      <td>4.7</td>\n",
       "      <td>4,887,519,660,744.9</td>\n",
       "      <td>2.5</td>\n",
       "      <td>69,818,262,802.1</td>\n",
       "      <td>75,072,263,386.7</td>\n",
       "      <td>8,299.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>80.7</td>\n",
       "      <td>2000</td>\n",
       "      <td>Japan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>126,631,000.0</td>\n",
       "      <td>64,416,945.0</td>\n",
       "      <td>62,214,055.0</td>\n",
       "      <td>9.3</td>\n",
       "      <td>7.8</td>\n",
       "      <td>9.0</td>\n",
       "      <td>31.9</td>\n",
       "      <td>5.2</td>\n",
       "      <td>5.8</td>\n",
       "      <td>22.1</td>\n",
       "      <td>4.7</td>\n",
       "      <td>4,562,078,822,335.5</td>\n",
       "      <td>-0.8</td>\n",
       "      <td>54,865,030,007.0</td>\n",
       "      <td>73,017,589,819.0</td>\n",
       "      <td>8,147.4</td>\n",
       "      <td>3.8</td>\n",
       "      <td>80.6</td>\n",
       "      <td>1999</td>\n",
       "      <td>Japan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>126,400,000.0</td>\n",
       "      <td>64,270,952.0</td>\n",
       "      <td>62,129,048.0</td>\n",
       "      <td>9.6</td>\n",
       "      <td>7.5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>32.2</td>\n",
       "      <td>5.3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>22.8</td>\n",
       "      <td>4.1</td>\n",
       "      <td>4,032,509,760,872.9</td>\n",
       "      <td>-1.9</td>\n",
       "      <td>48,839,122,632.9</td>\n",
       "      <td>65,831,559,150.0</td>\n",
       "      <td>8,004.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>79.4</td>\n",
       "      <td>1998</td>\n",
       "      <td>Japan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>126,057,000.0</td>\n",
       "      <td>64,068,675.0</td>\n",
       "      <td>61,988,325.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>7.3</td>\n",
       "      <td>9.0</td>\n",
       "      <td>33.3</td>\n",
       "      <td>5.4</td>\n",
       "      <td>6.0</td>\n",
       "      <td>24.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>4,414,732,843,544.4</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>56,500,943,459.4</td>\n",
       "      <td>68,751,451,555.4</td>\n",
       "      <td>7,991.0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>80.4</td>\n",
       "      <td>1997</td>\n",
       "      <td>Japan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>125,757,000.0</td>\n",
       "      <td>63,890,191.0</td>\n",
       "      <td>61,866,809.0</td>\n",
       "      <td>9.6</td>\n",
       "      <td>7.1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>33.4</td>\n",
       "      <td>5.5</td>\n",
       "      <td>6.3</td>\n",
       "      <td>24.7</td>\n",
       "      <td>3.4</td>\n",
       "      <td>4,833,712,542,207.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>56,995,323,550.2</td>\n",
       "      <td>81,929,341,206.2</td>\n",
       "      <td>7,835.4</td>\n",
       "      <td>3.8</td>\n",
       "      <td>81.2</td>\n",
       "      <td>1996</td>\n",
       "      <td>Japan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Total Population  Female Population  Male Population  Birth Rate  \\\n",
       "0      327,167,434.0      165,285,036.0    161,882,398.0        15.7   \n",
       "1      325,147,121.0      164,275,323.0    160,871,798.0        11.8   \n",
       "2      323,071,342.0      163,245,729.0    159,825,613.0        12.2   \n",
       "3      320,742,673.0      162,097,641.0    158,645,032.0        12.4   \n",
       "4      318,386,421.0      160,946,480.0    157,439,941.0        12.5   \n",
       "..               ...                ...              ...         ...   \n",
       "18     126,843,000.0       64,552,936.0     62,290,064.0         9.4   \n",
       "19     126,631,000.0       64,416,945.0     62,214,055.0         9.3   \n",
       "20     126,400,000.0       64,270,952.0     62,129,048.0         9.6   \n",
       "21     126,057,000.0       64,068,675.0     61,988,325.0         9.5   \n",
       "22     125,757,000.0       63,890,191.0     61,866,809.0         9.6   \n",
       "\n",
       "    Death Rate  Compulsory Education Dur.  Employment in Industry(%)  \\\n",
       "0          8.7                       12.0                       19.4   \n",
       "1          8.5                       12.0                       19.7   \n",
       "2          8.5                       12.0                       19.8   \n",
       "3          8.4                       12.0                       19.9   \n",
       "4          8.2                       12.0                       20.0   \n",
       "..         ...                        ...                        ...   \n",
       "18         7.7                        9.0                       31.4   \n",
       "19         7.8                        9.0                       31.9   \n",
       "20         7.5                        9.0                       32.2   \n",
       "21         7.3                        9.0                       33.3   \n",
       "22         7.1                        9.0                       33.4   \n",
       "\n",
       "    Employment in Agriculture(%)  Female Employment in Agriculture(%)  \\\n",
       "0                            1.4                                  0.8   \n",
       "1                            1.4                                  0.8   \n",
       "2                            1.4                                  0.8   \n",
       "3                            1.4                                  0.7   \n",
       "4                            1.4                                  0.7   \n",
       "..                           ...                                  ...   \n",
       "18                           5.1                                  5.5   \n",
       "19                           5.2                                  5.8   \n",
       "20                           5.3                                  6.0   \n",
       "21                           5.4                                  6.0   \n",
       "22                           5.5                                  6.3   \n",
       "\n",
       "    Female Employment in Industry(%)  Unemployment(%)           GDP in USD  \\\n",
       "0                                8.5              3.9 20,544,343,456,936.5   \n",
       "1                                8.7              4.4 19,485,393,853,000.0   \n",
       "2                                8.7              4.9 18,707,188,235,000.0   \n",
       "3                                8.8              5.3 18,219,297,584,000.0   \n",
       "4                                8.9              6.2 17,521,746,534,000.0   \n",
       "..                               ...              ...                  ...   \n",
       "18                              21.6              4.7  4,887,519,660,744.9   \n",
       "19                              22.1              4.7  4,562,078,822,335.5   \n",
       "20                              22.8              4.1  4,032,509,760,872.9   \n",
       "21                              24.2              3.4  4,414,732,843,544.4   \n",
       "22                              24.7              3.4  4,833,712,542,207.1   \n",
       "\n",
       "    National Income per Capita  Net income from Abroad  \\\n",
       "0                          1.7       267,900,000,000.0   \n",
       "1                          2.2       243,296,000,000.0   \n",
       "2                         -0.1       218,168,000,000.0   \n",
       "3                          2.4       224,626,000,000.0   \n",
       "4                          2.9       241,062,000,000.0   \n",
       "..                         ...                     ...   \n",
       "18                         2.5        69,818,262,802.1   \n",
       "19                        -0.8        54,865,030,007.0   \n",
       "20                        -1.9        48,839,122,632.9   \n",
       "21                        -0.0        56,500,943,459.4   \n",
       "22                         3.0        56,995,323,550.2   \n",
       "\n",
       "    Agriculture value added(in USD)  \\\n",
       "0                 152,778,873,809.5   \n",
       "1                 178,579,605,000.0   \n",
       "2                 175,923,170,000.0   \n",
       "3                 189,947,276,000.0   \n",
       "4                 208,273,224,000.0   \n",
       "..                              ...   \n",
       "18                 75,072,263,386.7   \n",
       "19                 73,017,589,819.0   \n",
       "20                 65,831,559,150.0   \n",
       "21                 68,751,451,555.4   \n",
       "22                 81,929,341,206.2   \n",
       "\n",
       "    Electric Power Consumption(kWH per capita)  \\\n",
       "0                                     10,318.0   \n",
       "1                                     10,318.0   \n",
       "2                                     10,318.0   \n",
       "3                                     10,318.0   \n",
       "4                                     12,994.0   \n",
       "..                                         ...   \n",
       "18                                     8,299.4   \n",
       "19                                     8,147.4   \n",
       "20                                     8,004.0   \n",
       "21                                     7,991.0   \n",
       "22                                     7,835.4   \n",
       "\n",
       "    Renewable Energy Consumption (%)  Fossil Fuel Consumption (%)  Year  \\\n",
       "0                                6.0                         89.3  2018   \n",
       "1                                6.0                         89.3  2017   \n",
       "2                                6.0                         89.3  2016   \n",
       "3                                8.7                         82.4  2015   \n",
       "4                                8.8                         83.1  2014   \n",
       "..                               ...                          ...   ...   \n",
       "18                               3.9                         80.7  2000   \n",
       "19                               3.8                         80.6  1999   \n",
       "20                               4.0                         79.4  1998   \n",
       "21                               4.1                         80.4  1997   \n",
       "22                               3.8                         81.2  1996   \n",
       "\n",
       "   Country  \n",
       "0      USA  \n",
       "1      USA  \n",
       "2      USA  \n",
       "3      USA  \n",
       "4      USA  \n",
       "..     ...  \n",
       "18   Japan  \n",
       "19   Japan  \n",
       "20   Japan  \n",
       "21   Japan  \n",
       "22   Japan  \n",
       "\n",
       "[200 rows x 20 columns]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_df=pd.concat(list_cleaned_df,sort=False)\n",
    "combined_df.head(200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Descriptive Statistics\n",
    "\n",
    "We can now analyse the data we have loaded. Descriptive statistics show the characteristics of numerical features. It gives information such as count, mean, mininum and maximum values etc. Hence we exclude the categorical features Year and Country from the combined DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total Population</th>\n",
       "      <th>Female Population</th>\n",
       "      <th>Male Population</th>\n",
       "      <th>Birth Rate</th>\n",
       "      <th>Death Rate</th>\n",
       "      <th>Compulsory Education Dur.</th>\n",
       "      <th>Employment in Industry(%)</th>\n",
       "      <th>Employment in Agriculture(%)</th>\n",
       "      <th>Female Employment in Agriculture(%)</th>\n",
       "      <th>Female Employment in Industry(%)</th>\n",
       "      <th>Unemployment(%)</th>\n",
       "      <th>GDP in USD</th>\n",
       "      <th>National Income per Capita</th>\n",
       "      <th>Net income from Abroad</th>\n",
       "      <th>Agriculture value added(in USD)</th>\n",
       "      <th>Electric Power Consumption(kWH per capita)</th>\n",
       "      <th>Renewable Energy Consumption (%)</th>\n",
       "      <th>Fossil Fuel Consumption (%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>413.0</td>\n",
       "      <td>413.0</td>\n",
       "      <td>413.0</td>\n",
       "      <td>413.0</td>\n",
       "      <td>413.0</td>\n",
       "      <td>413.0</td>\n",
       "      <td>413.0</td>\n",
       "      <td>413.0</td>\n",
       "      <td>413.0</td>\n",
       "      <td>413.0</td>\n",
       "      <td>413.0</td>\n",
       "      <td>413.0</td>\n",
       "      <td>413.0</td>\n",
       "      <td>413.0</td>\n",
       "      <td>413.0</td>\n",
       "      <td>413.0</td>\n",
       "      <td>413.0</td>\n",
       "      <td>413.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>349,611,508.7</td>\n",
       "      <td>170,880,636.9</td>\n",
       "      <td>178,730,871.8</td>\n",
       "      <td>19.9</td>\n",
       "      <td>9.4</td>\n",
       "      <td>9.7</td>\n",
       "      <td>24.1</td>\n",
       "      <td>16.6</td>\n",
       "      <td>17.7</td>\n",
       "      <td>15.4</td>\n",
       "      <td>8.4</td>\n",
       "      <td>2,100,409,242,193.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>12,542,248,900.4</td>\n",
       "      <td>84,920,838,838.5</td>\n",
       "      <td>5,650.1</td>\n",
       "      <td>17.6</td>\n",
       "      <td>80.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>430,522,054.6</td>\n",
       "      <td>207,779,594.4</td>\n",
       "      <td>222,772,709.2</td>\n",
       "      <td>9.3</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.3</td>\n",
       "      <td>3.7</td>\n",
       "      <td>21.1</td>\n",
       "      <td>24.9</td>\n",
       "      <td>6.1</td>\n",
       "      <td>8.2</td>\n",
       "      <td>3,615,163,477,805.6</td>\n",
       "      <td>3.8</td>\n",
       "      <td>50,321,336,949.2</td>\n",
       "      <td>137,934,730,440.0</td>\n",
       "      <td>4,811.2</td>\n",
       "      <td>15.1</td>\n",
       "      <td>13.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>17,099,840.0</td>\n",
       "      <td>8,574,108.0</td>\n",
       "      <td>8,525,732.0</td>\n",
       "      <td>7.6</td>\n",
       "      <td>5.9</td>\n",
       "      <td>8.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>7.4</td>\n",
       "      <td>2.1</td>\n",
       "      <td>7,575,396,972.9</td>\n",
       "      <td>-25.9</td>\n",
       "      <td>-77,826,915,006.9</td>\n",
       "      <td>807,799,677.2</td>\n",
       "      <td>97.8</td>\n",
       "      <td>0.6</td>\n",
       "      <td>35.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>47,291,610.0</td>\n",
       "      <td>23,976,130.0</td>\n",
       "      <td>23,315,480.0</td>\n",
       "      <td>12.8</td>\n",
       "      <td>7.2</td>\n",
       "      <td>9.0</td>\n",
       "      <td>21.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.6</td>\n",
       "      <td>3.9</td>\n",
       "      <td>137,774,755,609.1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>-10,759,692,566.3</td>\n",
       "      <td>16,730,104,729.7</td>\n",
       "      <td>1,081.6</td>\n",
       "      <td>4.3</td>\n",
       "      <td>75.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>123,116,000.0</td>\n",
       "      <td>62,425,974.0</td>\n",
       "      <td>60,690,026.0</td>\n",
       "      <td>16.6</td>\n",
       "      <td>8.6</td>\n",
       "      <td>9.0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>4.7</td>\n",
       "      <td>4.9</td>\n",
       "      <td>14.7</td>\n",
       "      <td>5.8</td>\n",
       "      <td>543,300,000,000.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>-2,019,508,809.8</td>\n",
       "      <td>38,018,588,193.9</td>\n",
       "      <td>4,573.0</td>\n",
       "      <td>17.2</td>\n",
       "      <td>85.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>608,802,600.0</td>\n",
       "      <td>293,362,998.0</td>\n",
       "      <td>315,439,602.0</td>\n",
       "      <td>23.7</td>\n",
       "      <td>10.9</td>\n",
       "      <td>11.0</td>\n",
       "      <td>26.5</td>\n",
       "      <td>43.2</td>\n",
       "      <td>40.3</td>\n",
       "      <td>18.8</td>\n",
       "      <td>7.9</td>\n",
       "      <td>2,290,432,075,123.8</td>\n",
       "      <td>4.7</td>\n",
       "      <td>12,619,000,000.0</td>\n",
       "      <td>101,244,489,721.0</td>\n",
       "      <td>8,266.1</td>\n",
       "      <td>22.9</td>\n",
       "      <td>89.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>1,392,730,000.0</td>\n",
       "      <td>677,972,245.0</td>\n",
       "      <td>714,757,755.0</td>\n",
       "      <td>43.4</td>\n",
       "      <td>25.4</td>\n",
       "      <td>12.0</td>\n",
       "      <td>34.8</td>\n",
       "      <td>63.0</td>\n",
       "      <td>76.3</td>\n",
       "      <td>29.7</td>\n",
       "      <td>33.5</td>\n",
       "      <td>20,544,343,456,936.5</td>\n",
       "      <td>18.5</td>\n",
       "      <td>267,900,000,000.0</td>\n",
       "      <td>978,452,524,165.8</td>\n",
       "      <td>17,264.7</td>\n",
       "      <td>58.7</td>\n",
       "      <td>99.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Total Population  Female Population  Male Population  Birth Rate  \\\n",
       "count             413.0              413.0            413.0       413.0   \n",
       "mean      349,611,508.7      170,880,636.9    178,730,871.8        19.9   \n",
       "std       430,522,054.6      207,779,594.4    222,772,709.2         9.3   \n",
       "min        17,099,840.0        8,574,108.0      8,525,732.0         7.6   \n",
       "25%        47,291,610.0       23,976,130.0     23,315,480.0        12.8   \n",
       "50%       123,116,000.0       62,425,974.0     60,690,026.0        16.6   \n",
       "75%       608,802,600.0      293,362,998.0    315,439,602.0        23.7   \n",
       "max     1,392,730,000.0      677,972,245.0    714,757,755.0        43.4   \n",
       "\n",
       "       Death Rate  Compulsory Education Dur.  Employment in Industry(%)  \\\n",
       "count       413.0                      413.0                      413.0   \n",
       "mean          9.4                        9.7                       24.1   \n",
       "std           3.1                        1.3                        3.7   \n",
       "min           5.9                        8.0                       15.3   \n",
       "25%           7.2                        9.0                       21.8   \n",
       "50%           8.6                        9.0                       23.3   \n",
       "75%          10.9                       11.0                       26.5   \n",
       "max          25.4                       12.0                       34.8   \n",
       "\n",
       "       Employment in Agriculture(%)  Female Employment in Agriculture(%)  \\\n",
       "count                         413.0                                413.0   \n",
       "mean                           16.6                                 17.7   \n",
       "std                            21.1                                 24.9   \n",
       "min                             1.1                                  0.6   \n",
       "25%                             1.8                                  1.0   \n",
       "50%                             4.7                                  4.9   \n",
       "75%                            43.2                                 40.3   \n",
       "max                            63.0                                 76.3   \n",
       "\n",
       "       Female Employment in Industry(%)  Unemployment(%)           GDP in USD  \\\n",
       "count                             413.0            413.0                413.0   \n",
       "mean                               15.4              8.4  2,100,409,242,193.0   \n",
       "std                                 6.1              8.2  3,615,163,477,805.6   \n",
       "min                                 7.4              2.1      7,575,396,972.9   \n",
       "25%                                10.6              3.9    137,774,755,609.1   \n",
       "50%                                14.7              5.8    543,300,000,000.0   \n",
       "75%                                18.8              7.9  2,290,432,075,123.8   \n",
       "max                                29.7             33.5 20,544,343,456,936.5   \n",
       "\n",
       "       National Income per Capita  Net income from Abroad  \\\n",
       "count                       413.0                   413.0   \n",
       "mean                          2.9        12,542,248,900.4   \n",
       "std                           3.8        50,321,336,949.2   \n",
       "min                         -25.9       -77,826,915,006.9   \n",
       "25%                           1.1       -10,759,692,566.3   \n",
       "50%                           2.4        -2,019,508,809.8   \n",
       "75%                           4.7        12,619,000,000.0   \n",
       "max                          18.5       267,900,000,000.0   \n",
       "\n",
       "       Agriculture value added(in USD)  \\\n",
       "count                            413.0   \n",
       "mean                  84,920,838,838.5   \n",
       "std                  137,934,730,440.0   \n",
       "min                      807,799,677.2   \n",
       "25%                   16,730,104,729.7   \n",
       "50%                   38,018,588,193.9   \n",
       "75%                  101,244,489,721.0   \n",
       "max                  978,452,524,165.8   \n",
       "\n",
       "       Electric Power Consumption(kWH per capita)  \\\n",
       "count                                       413.0   \n",
       "mean                                      5,650.1   \n",
       "std                                       4,811.2   \n",
       "min                                          97.8   \n",
       "25%                                       1,081.6   \n",
       "50%                                       4,573.0   \n",
       "75%                                       8,266.1   \n",
       "max                                      17,264.7   \n",
       "\n",
       "       Renewable Energy Consumption (%)  Fossil Fuel Consumption (%)  \n",
       "count                             413.0                        413.0  \n",
       "mean                               17.6                         80.9  \n",
       "std                                15.1                         13.5  \n",
       "min                                 0.6                         35.1  \n",
       "25%                                 4.3                         75.4  \n",
       "50%                                17.2                         85.3  \n",
       "75%                                22.9                         89.0  \n",
       "max                                58.7                         99.5  "
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a copy so that the original DF is not affected\n",
    "# drop the columns year and country\n",
    "df_copy=combined_df.drop(['Year', 'Country'], axis='columns')\n",
    "df_copy.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can oberve that there are total **413 records** across all the countries. The values for each of the countries are varied. For example, consider female employment percentage in agriculture. The minimum value is 0.5% and the maximum is 76%. This means that some countries have a very low percentage of females in agriculture whereas some other countries have a good number of females employed in this field.\n",
    "\n",
    "Also, on observing the national income per capita, we can see that the lowest value is around -26. On observing the data carefully it was found that some countries do have a negative national income in some years. This may be probably because the country was in losses and there was no income as such."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation Matrix\n",
    "\n",
    "A correlation matrix is a table that represents the correlation coefficients between different variables. It makes it easy to analyse which variables have a correlation with each other. This can be helpful in finding the relationship between the variables and further analysing them through different plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a copy\n",
    "df_cleaned_us_copy=df_cleaned_us.copy()\n",
    "# Exclude the categorical features from the matrix\n",
    "df_cleaned_us_copy.drop(['Year', 'Country'], inplace=True, axis='columns')\n",
    "\n",
    "# plot a correlation matrix\n",
    "fig, ax = plt.subplots(figsize=(10,10))\n",
    "sns.heatmap(df_cleaned_us_copy.corr(), cmap='RdBu', center=0,ax=ax)\n",
    "plt.savefig('correlation_us.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above is the correlation matrix between features for USA.\n",
    "It is found that ***Compulsory Education Duration*** feature does not have any correlation with other features, hence, it can be excluded from our analysis. Also, ***Income per capita*** has a very weak correlation with other features and can be excluded.\n",
    "I have checked the correlation matrices for each of the countries and found that the fields Income per capita and Compulsory Education Duration have the same kind of correlation with other features. Only one correlation matrix has been included here as an example."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis Using Plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparing Population of Countries in 2000 and 2018:\n",
    "\n",
    "Since we have the data for different countries, we can compare how the population for different countries has changed in 2018 - the most recent year, as opposed to 2000. The following code prepares two DataFrames - one for each year under consideration. We extract the column Total Population. The difference in population has been shown using a **grouped bar** chart."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data of Total Population for 2000 and 2018 for all countries: \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2000</th>\n",
       "      <th>2018</th>\n",
       "      <th>Countries</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>USA</td>\n",
       "      <td>282,162,411.0</td>\n",
       "      <td>327,167,434.0</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>India</td>\n",
       "      <td>1,056,575,549.0</td>\n",
       "      <td>1,352,617,328.0</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>China</td>\n",
       "      <td>1,262,645,000.0</td>\n",
       "      <td>1,392,730,000.0</td>\n",
       "      <td>China</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Japan</td>\n",
       "      <td>126,843,000.0</td>\n",
       "      <td>126,529,100.0</td>\n",
       "      <td>Japan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Canada</td>\n",
       "      <td>30,685,730.0</td>\n",
       "      <td>37,058,856.0</td>\n",
       "      <td>Canada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Great Britain</td>\n",
       "      <td>58,892,514.0</td>\n",
       "      <td>66,488,991.0</td>\n",
       "      <td>Great Britain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>South Africa</td>\n",
       "      <td>44,967,708.0</td>\n",
       "      <td>57,779,622.0</td>\n",
       "      <td>South Africa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         2000            2018      Countries\n",
       "USA             282,162,411.0   327,167,434.0            USA\n",
       "India         1,056,575,549.0 1,352,617,328.0          India\n",
       "China         1,262,645,000.0 1,392,730,000.0          China\n",
       "Japan           126,843,000.0   126,529,100.0          Japan\n",
       "Canada           30,685,730.0    37,058,856.0         Canada\n",
       "Great Britain    58,892,514.0    66,488,991.0  Great Britain\n",
       "South Africa     44,967,708.0    57,779,622.0   South Africa"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# refer to the list of countries\n",
    "list_countries = COUNTRY_LIST\n",
    "# intialise two dataframes df_00- year 2000, df_18 - year 2018\n",
    "df_00 = pd.DataFrame()\n",
    "df_18 = pd.DataFrame()\n",
    "\n",
    "# for each dataframe in the list of cleaned dataframes\n",
    "for i,df in enumerate(list_cleaned_df):\n",
    "    # pick the value of Total Population for year 2000 and 2018\n",
    "    df_00[list_countries[i]] = df[df['Year'] == 2000][\"Total Population\"]\n",
    "    df_18[list_countries[i]] = df[df['Year'] == 2018][\"Total Population\"]\n",
    "\n",
    "# The resulting dataframes have the countries as columns and the two rows each for 2000 and 2018\n",
    "# To be able to draw a grouped bar plot we need the years as columns, hence we take a transpose\n",
    "df_00 = df_00.T\n",
    "df_18 = df_18.T\n",
    "\n",
    "pd.options.display.float_format = '{:,.1f}'.format  # set other global format\n",
    "\n",
    "# rename the columns to the year\n",
    "df_00 = df_00.rename(columns={18 : 2000})\n",
    "df_18 = df_18.rename(columns={0 : 2018})\n",
    "\n",
    "# join the dataframes for both the years\n",
    "df_both_years= df_00.join(df_18)\n",
    "\n",
    "# the index is the Country name, hence we add it as a column into the data frame.\n",
    "df_both_years['Countries'] = df_both_years.index\n",
    "\n",
    "# drop the original index\n",
    "df_both_years.reset_index(drop=True)\n",
    "\n",
    "print(\"Data of Total Population for 2000 and 2018 for all countries: \")\n",
    "display(df_both_years)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26b1a6d3048>"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 504x360 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7, 5))\n",
    "# plot the chart using matplotlib.pyplot library\n",
    "df_both_years.plot(kind='bar',x='Countries',y=[2000, 2018])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that India and China both have the highest population in both the years. However, the increase in population for India in year 2018 is more as compared to the increase in China making their population almost comparable. Canada has the least population amongst all with marginal increase. One striking observation is for Japan - the population is the same in both 2000 and 2018 signifying good population control mechanisms in the country."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Average Birth Rate and Death Rate for countries across all the years\n",
    "\n",
    "We have compared the total population in 2000 and 2018 for various countries. Now let us compare the average birth rate and death rate for different countries. Using the combined dataframe that contains the data for all the features for all the countries, we have **grouped the rows by country** and found the mean birth rate and death rate. I have used a bar chart and sorted the countries in descending order of birth rate and death rate.\n",
    "\n",
    "The following function has been implemented:\n",
    "* **group_df():** This function takes a column name as a parameter and returns a new DF with the data for countrywise average for the specified feature.\n",
    "* **plot_bar():** This function takes a dataframe as an argument and plots a bar chart based on the X and Y features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "def group_df(feature):\n",
    "    # create a new dataframe\n",
    "    df_grouped=pd.DataFrame()\n",
    "\n",
    "    # find average for each country\n",
    "    df_grouped['Avg. ' + feature]=combined_df.groupby('Country')[feature].mean()\n",
    "\n",
    "    # set the index as a column - countries\n",
    "    df_grouped['Country']=df_grouped.index\n",
    "\n",
    "    # drop the index\n",
    "    df_grouped.reset_index(drop=True, inplace=True)\n",
    "\n",
    "    # sort the rows based of Avg Birth rate\n",
    "    df_grouped.sort_values('Avg. '+feature, inplace=True, ascending=False)\n",
    "\n",
    "    print(\"Avg. \" + feature)\n",
    "    display(df_grouped)\n",
    "    \n",
    "    return df_grouped\n",
    "\n",
    "def plot_bar(df, x_feature, y_feature):\n",
    "    # bar plot\n",
    "    plt.figure(figsize=(8, 5))\n",
    "    sns.set(style=\"whitegrid\")\n",
    "    ax = sns.barplot(\n",
    "        data= df,\n",
    "        x= x_feature,\n",
    "        y= \"Avg. \" + y_feature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Avg. Birth Rate\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Avg. Birth Rate</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>31.3</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>30.8</td>\n",
       "      <td>South Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>20.5</td>\n",
       "      <td>China</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>15.7</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>14.7</td>\n",
       "      <td>Canada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>13.7</td>\n",
       "      <td>Great Britain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>12.4</td>\n",
       "      <td>Japan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Avg. Birth Rate        Country\n",
       "3             31.3          India\n",
       "5             30.8   South Africa\n",
       "1             20.5          China\n",
       "6             15.7            USA\n",
       "0             14.7         Canada\n",
       "2             13.7  Great Britain\n",
       "4             12.4          Japan"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========================================================\n",
      "Avg. Death Rate\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Avg. Death Rate</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>12.3</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>11.9</td>\n",
       "      <td>South Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>10.9</td>\n",
       "      <td>Great Britain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>8.7</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>7.6</td>\n",
       "      <td>China</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>7.5</td>\n",
       "      <td>Japan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>Canada</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Avg. Death Rate        Country\n",
       "3             12.3          India\n",
       "5             11.9   South Africa\n",
       "2             10.9  Great Britain\n",
       "6              8.7            USA\n",
       "1              7.6          China\n",
       "4              7.5          Japan\n",
       "0              7.2         Canada"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_birth=group_df('Birth Rate')\n",
    "plot_bar(df_birth, 'Country', 'Birth Rate')\n",
    "\n",
    "print(\"========================================================\")\n",
    "df_death=group_df('Death Rate')\n",
    "plot_bar(df_death, 'Country', 'Death Rate')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above two plots we can see that India has the highest Avg Birth Rate as well as Avg Death Rate which is interesting since it had the second highest population in 2018. Similar is the trend for South Africa which is again surprising since South Africa had a very low population. For China, the Birth Rate is higher than the Death Rate thus leading to the highest population in the world. For Great Britain the Death Rate is much higher than the Average Birth Rate, leading to low population."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GDP for all countries in the last 10 years\n",
    "\n",
    "I have used a line chart to show how the GDP for various countries has varied over the last 10 years. We extract the columns Year, GDP in USD and Country from each of the country's dataframe and store it in a smaller dataframe for plotting.\n",
    "A **line plot** clearly shows the trends over a period of time and can also be used to compare the trends of different categories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Few records from the Dataframe containing Year, GDP and Country:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>GDP in USD</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2018</td>\n",
       "      <td>2,718,732,231,257.6</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>2,652,242,857,923.9</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2016</td>\n",
       "      <td>2,290,432,075,123.8</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2015</td>\n",
       "      <td>2,103,587,813,812.8</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2014</td>\n",
       "      <td>2,039,127,446,298.6</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year          GDP in USD Country\n",
       "0  2018 2,718,732,231,257.6   India\n",
       "1  2017 2,652,242,857,923.9   India\n",
       "2  2016 2,290,432,075,123.8   India\n",
       "3  2015 2,103,587,813,812.8   India\n",
       "4  2014 2,039,127,446,298.6   India"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# function to to form a dataframe with Year, GDP and Country\n",
    "def extract_columns(df_cleaned):\n",
    "    df=pd.DataFrame()\n",
    "    # pick data for the recent 10 years, note that the data sorted in descending order of year\n",
    "    df['Year']=df_cleaned.loc[:10, 'Year']\n",
    "    df['GDP in USD']=df_cleaned.loc[:10, 'GDP in USD']\n",
    "    df['Country']=df_cleaned.loc[:10, 'Country']\n",
    "    return df\n",
    "\n",
    "# function to fetch a single dataframe with 3 features from each country\n",
    "def form_gdp_df():\n",
    "    # function call to extract_columns()\n",
    "    indf=extract_columns(df_cleaned_in)\n",
    "    usdf=extract_columns(df_cleaned_us)\n",
    "    cndf=extract_columns(df_cleaned_cn)\n",
    "    jpdf=extract_columns(df_cleaned_jp)\n",
    "    cadf=extract_columns(df_cleaned_ca)\n",
    "    gbdf=extract_columns(df_cleaned_gb)\n",
    "    zadf=extract_columns(df_cleaned_za)\n",
    "    # combine the 7 dfs into a single df with 3 columns\n",
    "    # we ignore the original index\n",
    "    gdp_df=pd.concat([indf, usdf, cndf, jpdf, cadf, gbdf, zadf], ignore_index=True)\n",
    "    return gdp_df\n",
    "\n",
    "# get the combined DF\n",
    "gdp_df=form_gdp_df()\n",
    "\n",
    "print(\"Few records from the Dataframe containing Year, GDP and Country:\")\n",
    "display(gdp_df.head())\n",
    "\n",
    "# set figure size\n",
    "plt.figure(figsize=(7, 5))\n",
    "sns.set(style=\"whitegrid\")\n",
    "# plot using seaborn library\n",
    "ax=sns.lineplot(x='Year', y='GDP in USD', hue='Country', style=\"Country\",palette=\"Set2\", markers=True, dashes=False, data=gdp_df, linewidth=2.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that USA has the highest GDP amongst all countries which is clear throughout the years. The GDP for China was low in the year 2008 and shows a significant rise upto 2018 which is attributed to their progress in various fields like manufacturing, however, in comparison to USA, it is still very less.\n",
    "\n",
    "For Japan, the GDP shows a slight rise upto 2012 which may be due to Japan's growth strategy that was adopted to pull it out from deflation, but thereafter there is a dip in the GDP."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Electric Power Consumption vs Population for India and China\n",
    "\n",
    "As seen from the population grouped bar plot above, India and China are the most populated countries. Let us examine the Electric power consumption in these countries. For this task, I have extracted the Population, Country and Electric Power consumption from the dataframes for these two countries and visualised them using a scatter plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Few records from the selected features: \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total Population</th>\n",
       "      <th>Electric Power Consumption(kWH per capita)</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1,352,617,328.0</td>\n",
       "      <td>335.2</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1,338,658,835.0</td>\n",
       "      <td>335.2</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1,324,509,589.0</td>\n",
       "      <td>335.2</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1,310,152,403.0</td>\n",
       "      <td>335.2</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1,295,604,184.0</td>\n",
       "      <td>804.5</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total Population  Electric Power Consumption(kWH per capita) Country\n",
       "0   1,352,617,328.0                                       335.2   India\n",
       "1   1,338,658,835.0                                       335.2   India\n",
       "2   1,324,509,589.0                                       335.2   India\n",
       "3   1,310,152,403.0                                       335.2   India\n",
       "4   1,295,604,184.0                                       804.5   India"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# function to extract specific columns from the DFs for India and China\n",
    "def form_in_cn_df():\n",
    "    # for India\n",
    "    indf=df_cleaned_in[['Total Population', 'Electric Power Consumption(kWH per capita)', 'Country']]\n",
    "    # for China\n",
    "    cndf=df_cleaned_cn[['Total Population', 'Electric Power Consumption(kWH per capita)', 'Country']]\n",
    "    # combine the two dataframes\n",
    "    in_cn_df=pd.concat([indf, cndf])\n",
    "    return in_cn_df\n",
    "\n",
    "# get the desired data\n",
    "in_cn_df=form_in_cn_df()\n",
    "print(\"Few records from the selected features: \")\n",
    "display(in_cn_df.head())\n",
    "\n",
    "# scatter plot\n",
    "plt.figure(figsize=(7, 5))\n",
    "sns.set(style=\"whitegrid\")\n",
    "ax=sns.scatterplot(x='Total Population', y='Electric Power Consumption(kWH per capita)', hue='Country', palette=\"bright\", data=in_cn_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above plot it is clear that with the increase in the population, the electric power consumption is bound to increase. This is evident from the sudden shooting up of the electric power consumption of China. For India there has been a gradual increase in the consumption with population.The constant values in both the lines indicate the mean consumption."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Total Population vs Electric Power consumption for Canada upto 2015"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Canada is a country with very low population. Let us analyse if the population has any effect on the Electrical Energy Consumption in this country upto the year 2015. I have used a single line plot to see the trend with increase in population."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First few records of the data: \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Electric Power Consumption(kWH per capita)</th>\n",
       "      <th>Total Population</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>13,266.0</td>\n",
       "      <td>35,702,908.0</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>15,588.5</td>\n",
       "      <td>35,437,435.0</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>15,750.8</td>\n",
       "      <td>35,082,954.0</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>15,336.6</td>\n",
       "      <td>34,714,222.0</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>15,644.5</td>\n",
       "      <td>34,339,328.0</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Electric Power Consumption(kWH per capita)  Total Population  Year\n",
       "3                                    13,266.0      35,702,908.0  2015\n",
       "4                                    15,588.5      35,437,435.0  2014\n",
       "5                                    15,750.8      35,082,954.0  2013\n",
       "6                                    15,336.6      34,714,222.0  2012\n",
       "7                                    15,644.5      34,339,328.0  2011"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26b1b2836c8>"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# read the columns from the df for Canada\n",
    "df=df_cleaned_ca.loc[3:, ['Electric Power Consumption(kWH per capita)','Total Population', 'Year']]\n",
    "\n",
    "print(\"First few records of the data: \")\n",
    "display(df.head())\n",
    "\n",
    "# line plot\n",
    "plt.figure(figsize=(6, 5))\n",
    "sns.set(style=\"whitegrid\")\n",
    "sns.lineplot(x='Total Population', y='Electric Power Consumption(kWH per capita)', palette=\"colorblind\",data=df, linewidth=2.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above plot we can observe that for Canada the Electric power consumption has risen constantly with the rise in population. However, it remained constant after a point and thereafter reduced. Although the population in Canada is the least, it has a high electric power consumption which may be due to the extremely lower temperatures in the country."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Variation in different Energy Consumption over the years for Russia"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this analysis, I have chosen the energy consumption data for Russia over the years upto 2010 and plotted a multi-line chart to observe the trend."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pick the columns Year, and 3 different power consumptions from the dataframe for russia\n",
    "plt.plot(df_cleaned_in.loc[5:, ['Year']],df_cleaned_in.loc[5:, ['Electric Power Consumption(kWH per capita)']],'.-')\n",
    "plt.plot(df_cleaned_in.loc[5:, ['Year']],df_cleaned_in.loc[5:, ['Renewable Energy Consumption (%)']],'.-')\n",
    "plt.plot(df_cleaned_in.loc[5:, ['Year']],df_cleaned_in.loc[5:, ['Fossil Fuel Consumption (%)']],'.-')\n",
    "\n",
    "plt.legend(['Electric Power Consumption(kWH per capita)', 'Renewable Energy Consumption(%)', 'Fossil Fuel Consumption(%)'], loc='best')\n",
    "plt.title(\"Energy Consumption in Russia\\n\")\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Energy Consumption')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this plot, it can be observed that The Electric power consumption has increased significantly over time. On the other hand, the consumption for Fossil Fuels and Renewable Energy is low as compared to electrical energy. Fossil Fuel consumption seems to have increased between 2000 and 2010."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Employment in the Agricultural sector in Countries in the year 2015\n",
    "\n",
    "Let us examine which country has the most percentage of females employed in Agriculture in the year 2015. For this, I have used extracted Employment in Agriculture column from the DF for each country and displayed the values using a Bar plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Employment in Agriculture data for 2015: \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Employment in Agriculture(%)</th>\n",
       "      <th>Countries</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>45.6</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>28.6</td>\n",
       "      <td>China</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>5.6</td>\n",
       "      <td>South Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3.6</td>\n",
       "      <td>Japan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>Canada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1.1</td>\n",
       "      <td>Great Britain</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Employment in Agriculture(%)      Countries\n",
       "1                          45.6          India\n",
       "2                          28.6          China\n",
       "6                           5.6   South Africa\n",
       "3                           3.6          Japan\n",
       "4                           1.6         Canada\n",
       "0                           1.4            USA\n",
       "5                           1.1  Great Britain"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get the list of countries\n",
    "list_countries=COUNTRY_LIST\n",
    "\n",
    "df_agr_emp=pd.DataFrame()\n",
    "\n",
    "# iterate over the dataframe\n",
    "for i,df in enumerate(list_cleaned_df):\n",
    "    # store the % Employment in Agriculture for each country \n",
    "    df_agr_emp[list_countries[i]]=df[df['Year'] == 2015][\"Employment in Agriculture(%)\"]\n",
    "\n",
    "# take the transpose\n",
    "df_agr_emp=df_agr_emp.T\n",
    "\n",
    "pd.options.display.float_format = '{:,.1f}'.format  # set other global format\n",
    "\n",
    "# since we took a transpose to get countries as the rows, the row index shows the names of the countries.\n",
    "# Add it as a column in the DF\n",
    "df_agr_emp['Countries'] = df_agr_emp.index\n",
    "# drop the index\n",
    "df_agr_emp.reset_index(drop=True, inplace=True)\n",
    "# The index for Employment % in agriculture needs to be renamed to the actual column name\n",
    "df_agr_emp=df_agr_emp.rename(columns={3: \"Employment in Agriculture(%)\"})\n",
    "\n",
    "# sort the rows based in decreasing order of % Employment\n",
    "df_agr_emp.sort_values('Employment in Agriculture(%)', inplace=True, ascending=False)\n",
    "\n",
    "print(\"Employment in Agriculture data for 2015: \")\n",
    "display(df_agr_emp)\n",
    "\n",
    "# bar plot\n",
    "plt.figure(figsize=(8, 5))\n",
    "sns.set(style=\"whitegrid\")\n",
    "ax = sns.barplot(\n",
    "    data= df_agr_emp,\n",
    "    x= 'Countries',\n",
    "    y= 'Employment in Agriculture(%)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above plot it is evident that India has the highest percentage of Employment out of the total employed people in Agriculture. This clearly reflects the fact that India is an agricultural nation and has many food items harvested in the country itself. Followed by India is China. In China there is a shortage of Arable Land and hence farming is very labor intensive. Hence the high percentage of employment in agriculture. The rest of the countries have very less percentage of Employment in agriculture. Great Britain has the least percentage in Agriculture which can be attributed to the fact that it produces less than 60% of the food that is consumed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Summary and Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The World bank dataset consists of a number of features which can be used to develop interesting patterns in the data. I have collected the data for 7 countries for about 20 features. The data was pre-processed to remove columns with too many missing values and fill missing values with the average values for the features. Another way would have been to remove records with missing fields, however, this was not done since the data consists of records for 59 years only (one record per year per country). Hence, removing these values would have resulted in a very small dataset not suitable for analysis. Other efficient techniques can be used to find missing values to enhance the analysis.\n",
    "\n",
    "Some analysis was done between the Population counts, birth rate and death rate which revealed some interesting patterns about how the birth rate and death rate affect the overall population. Similarly, based on the nature of the countries, it was found that India has highest percentage of Employment in agriculture. Other analyses on the power consumption variation with the total population also showed that electrical power consumption is the highest in most countries which relates to their population.\n",
    "\n",
    "Further to this, more analysis can be performed on the Employment features such as percentage of males and females in each sector and comparisons can be made across years in different countries.\n",
    "\n",
    "This analysis can also be used to develop predictive models to predict the population growth based on Birth Rate and Death Rate or GDP growth based on employment. The analysis can be extended in the future by including other features from the API like Arable land, Education, Health services, etc. and make predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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